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31 | Completed (31) | 1,317 | Economics | -99 | M.Sc. in Economics | -99 | Data Scientist in Datacamp | -99 | Brazil | 1,001-2,000 employees | Business Analyst | -99 | 0 | 2 | 1 | 0 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 20 | 30 | 25 | 12 | 5 | 3 | Client often don't know what they want to learn about data. | Sometimes it's not possible to do what they want. | The client lack computational resources to tackle the problem | Where we can find the information? | Who can authorize this type of data colletcion? | Not enough time to collect the sample with the appropriate size | Too much missing values | Prolems with outliers | Problems with formats | Time to read the literature about theme | Understand qhat the model fits in the situation | Verify if the model is accurate | Overfit | Verify again the accuracy. | The model is simple enough for the user? | Show the model in a didatic way | Expose the features with care | Display the model in a easy mode to read | Verify the results | Retrain the model if necessary | Feeding the model in appropriate way | -99 | -99 | -99 | Problems with data collection and cleaning | Others tasks which competes the time | Search the appropriate methodology | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ |
34 | Completed (31) | 854 | -99 | Management | No | No | No | No | Brazil | More than 2,000 employees | Business Analyst | -99 | 2 | 2 | 1 | 1 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Human Resources | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Complex | Very Complex | Very Complex | Complex | Very Complex | Neutral | 30 | 12 | 12 | 12 | 12 | 12 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 70 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
36 | Completed (31) | 1,593 | Mathematics | Informatics | MSC Computer Science | PhD computer Science | Vários cursos in Coursera | -99 | Brazil | 51-250 employees | Project Lead / Project Manager | -99 | 20 | 5 | 5 | 1 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Meteorology | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Temperatura, Precipitation, COVID-19 patient outcome | quoted | Plant species | not quoted | -99 | quoted | Hospitals, time-series | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | GNN | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Very Complex | Complex | Complex | Neutral | Neutral | Complex | 20 | 10 | 15 | 20 | 5 | 15 | 15 | Learning the problem domain | Identify the task to be solved | Check the accuracy of findings | Find the relevant data sources | Build data extractors | -99 | Data cleaning | Data reconciliation | -99 | Select the best learning algo | Hyper-parametrization | Select the right data | Build test dataset | -99 | -99 | Prepare production environment | -99 | -99 | Assess metricts | Identify concept drifts | -99 | -99 | -99 | -99 | Data preparation | Prediction Task identification | Selecionar of learning algo | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | quoted | Literature | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 60 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Own approach | -99 |
57 | Completed (31) | 4,238 | Computer Science | Data science specialization | -99 | -99 | -99 | -99 | Germany | More than 2,000 employees | Solution Architect | -99 | 8 | 4 | 6 | 6 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | Using clusterization to find groups of credit card numbers potentially leaked | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | Low Relevance | Neutral | High Relevance | Extremely Relevant | Neutral | Complex | Easy | Easy | Neutral | Complex | Very Complex | Easy | 30 | 10 | 10 | 25 | 10 | 15 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 50 | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
46 | Completed (31) | 2,821 | Actuarial Science | Post Graduation in Data Science | M Sc in Data Science -ML models | no Ph D | no other certifications | -99 | Brazil | 501-1,000 employees | Data Scientist | -99 | 6 | 3 | 23 | 18 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | innovation | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | income models, claim and premium insurance models | quoted | probability models and scores, fraud and low default models | not quoted | -99 | quoted | KNN Apriori algortm and others | quoted | inbalanced dataset techniques | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | LGBM Catboosting | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Complex | Complex | Very Complex | Very Complex | Neutral | 15 | 10 | 10 | 15 | 15 | 25 | 10 | understand the pain and identify if ML is really needed to solve it | brainstorm the solution with ML | propose deadline to that project | is that data enough to that ML solution? | quality data validation | frequency and period (time) validation | wich event are we analysing ? | we need to cut or we need to cluster some kind of data? | we can work with all periods? why? (ex: test and validation data) | is that a classidication or regression problem? | what kind of technique seems bether to use for modelling? (metrics) | present and discuss metrics and distribution of results in this modelling | are metrics estable across the periods? (time validation) | confusion matrix | test and validation comparison | what kind of deploy is better? | how long it takes? | profit analysis | after deploy, data have the same ML results as predicted? | it will be necessary to review this ML solution? | -99 | -99 | -99 | -99 | understand the pain and identify if ML is really needed to solve it | we need to cut or we need to cluster some kind of data? | present and discuss metrics and distribution of results in this modelling | Sometimes | 20 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 80 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
53 | Completed (31) | 2,097 | Information System | -99 | M.Sc. in Applied Informatics | -99 | -99 | -99 | Brazil | 1,001-2,000 employees | Project Lead / Project Manager | -99 | 6 | 5 | 2 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Using classification to idenfify food in the images | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Complex | Complex | Complex | Complex | Complex | Neutral | 10 | 15 | 15 | 25 | 10 | 15 | 10 | not know about the problem | not knowing how to apply ML to the problem | -99 | insufficient amount of data | generate a meaningful data sample | -99 | sort the data | label the data | -99 | understand the models | apply the models | runtime | Select the best metrics | -99 | -99 | Not knowing how to deploy | -99 | -99 | I didn't apply it to my project | -99 | -99 | -99 | -99 | -99 | insufficient amount of data | apply the models | Not knowing how to deploy | Sometimes | 50 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
58 | Completed (31) | 1,696 | Computer Science | -99 | Computer Science | -99 | Microsoft Professional Program Data Science & ML specialization | -99 | Germany | 1-10 employees | Developer | -99 | 5 | 2 | 3 | 0 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | C# | quoted | Injury Prediction | quoted | Pedestrian Detection, Image Label Classification | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 35 | 20 | 5 | 5 | 5 | 5 | -99 | -99 | -99 | Availability | Quantity | Data Privacy | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Cost | Setup Difficulty | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data Availability | Sufficient Data Quantity | Deployment Costs for non-trivial ML projects | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://t.co/ |
64 | Completed (31) | 1,250 | Electrical and Electronics Engineering | -99 | M.Sc. in AI and Software Engineering | Computer science | Azure Associate AI Engineer, Azure Data Science Associate | -99 | Sweden | More than 2,000 employees | Other, which one? | Enterprise, system, solution architect | 40 | 15 | 5 | 1 | 50+ members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Data analysis for autonomous driving | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Neutral | Extremely Relevant | High Relevance | Extremely Relevant | Very Complex | Neutral | Complex | Easy | Very Complex | Complex | Complex | 30 | 10 | 20 | 5 | 20 | 5 | 10 | understand the context: domain, what decisions are made based on data in what activities to achieve what goals by what roles | reach consensus | resolve conflicts | Given context, define appropriate metrics | If humans are involved, how to avoid bias, motivate them to do it correctly etc. | Handling sensitive data, necessary to detect indirect discrimination | Handling disproportional classes of data | Perform data augmentation | -99 | Choose candidate approached | Partitition data, especially time series or unstructured data | -99 | Evaluate indirect/direct bias (e.g., indirect/direct discrimination), indirect is the hardest | Determine what metrics to use | -99 | Choose apropriate framework | -99 | -99 | Determine what metrics to monitor | Define alerters, thresholds | -99 | -99 | -99 | -99 | Basic: no proper engagement from management, no specific funding or no metrics to measure success | Understand the context | -99 | Sometimes | 50 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
65 | Completed (31) | 106 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
69 | Completed (31) | 79 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ |
70 | Completed (31) | 1,301 | Computer Science | -99 | -99 | -99 | -99 | -99 | Colombia | 51-250 employees | Other, which one? | Consultant | 6 | 1 | 2 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Very Complex | Complex | Neutral | Complex | Neutral | 15 | 20 | 15 | 20 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 23 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | http://m.facebook.com |
72 | Completed (31) | 2,128 | Statistics | Data Science | -99 | -99 | -99 | -99 | Brazil | 251-500 employees | Data Scientist | -99 | 0 | 1 | 1 | 5 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | SAS | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | Experimental designs, sampling methods to improve the test confiabilty, survival probabilities. | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | Kaplan Meyer, PCA, LDA, ANOVA, ARIMA seasonal, IRT | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Low Relevance | Neutral | Very Complex | Complex | Complex | Very Easy | Neutral | Neutral | Complex | 25 | 10 | 30 | 5 | 10 | 10 | 10 | Different concepts of same thing in the Communication | Different way to think about the problem | Different way to establish what can be the answer of the problem | Trash Data structures | No Data avaliable | Trash Data information | To many NULLs | Wrong data type | -99 | Choose the best model | Choose best sampling training data | Understand what the really model does | Choose correct technique to evaluate | See if it really answers the question problem | -99 | Some methods are expensive | Hard to make it accessible to specific group of people with login | -99 | Monitoring a model of another person (that u didn't work) | -99 | -99 | Understand statistics behind the models | Check if the model is really adequate using statiscal methods | Check is the sample is really representative and what is the confiability | Understand the problem | Data collection | Pre processing | Frequently | 70 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Notion/ Git hub | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 60 | quoted | quoted | quoted | quoted | not quoted | -99 | Yes, Please, specify | automl in R / driveML in R / databricks Jobs | https://lm.facebook.com/ |
75 | Completed (31) | 1,936 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
105 | Completed (31) | 2,183 | Mechanical Engineering | Control Theory, Information Technologies, Mechatronics | M.Sc. Robotics, Cognition, Intelligence | -99 | -99 | -99 | Germany | 1-10 employees | Project Lead / Project Manager | -99 | 12 | 6 | 4 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | identify properties and causes of human gait patterns | not quoted | -99 | quoted | extract step positions and times from floor sensor data | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Neutral | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Easy | Easy | Complex | Very Complex | Neutral | Neutral | 5 | 40 | 15 | 10 | 15 | 10 | 5 | estimating development time | converging expectations and possibilities | overthinking requirements | technical problems | communicating recording procedure | finding probands | keeping code understandable | missing values | -99 | finding a good input encoding | hyperparameter optimisation | -99 | bad real-world performance | validation leaking | -99 | creating robust APIs | -99 | -99 | graceful fault handling | -99 | -99 | -99 | -99 | -99 | converging expectations and possibilities | overthinking requirements | bad real-world performance | Frequently | 100 | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.google.com/ |
77 | Completed (31) | 930 | Physics | -99 | Particle physics | Particle physics | Artificial Intelligence applied to Geosciences at UFMG | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 20 | 5 | 5 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Very Complex | Complex | Easy | Easy | Easy | Complex | Neutral | 25 | 20 | 10 | 25 | 5 | 10 | 5 | Identifying the opportunity | -99 | -99 | Getting permission from the data owners | Identifying the best first oil field to use | Selecting the actual data | SEGY reading | -99 | -99 | Time to train with huge amounts of data (TB+) | Hyperparameter tuning | Model design | Selecting metrics | -99 | -99 | UI | Data reading constraints | -99 | UI | Metrics | -99 | -99 | -99 | -99 | Getting permission from the data owners | Time to train with huge amounts of data (TB+) | UI | Rarely | 20 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://statics.teams.cdn.office.net/ |
86 | Completed (31) | 1,509 | Engineering | Data Science, Aeroderivative Turbines, Database architecture | -99 | -99 | -99 | -99 | Brazil | 1-10 employees | Other, which one? | Director | 15 | 2 | 1 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | High Relevance | High Relevance | Neutral | Neutral | High Relevance | Neutral | Easy | Neutral | Neutral | Neutral | Neutral | Neutral | 20 | 15 | 10 | 20 | 15 | 10 | 10 | understand what to achieve | -99 | -99 | access to data | -99 | -99 | analyze data | -99 | -99 | discover better model | -99 | -99 | is the evaluation right? | -99 | -99 | deploy cheap as possible | -99 | -99 | develop tools to monitor | -99 | -99 | team integration | -99 | -99 | Dumb team lider | acess to data | team integration | I don't know | 50 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Director | not quoted | quoted | not quoted | quoted | not quoted | quoted | Beer please | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
111 | Completed (31) | 1,582 | Electrical and Electronics Engineering | Business information | Logistics | -99 | IBM Data science on Coursera | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 2 | 2 | 2 | 1 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predict the usage of a kind of vessel | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Complex | Very Complex | Neutral | I don't know | Very Complex | 10 | 50 | 20 | 10 | 5 | 5 | 0 | Deal with different interpretations of the problem | -99 | -99 | Discover where to get the data | Get all permissions | Conect with the bases | Understand the meaning of each data | Conect different data based | Different names or orthography for the same vessel | To decide te best model | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | To get the data to keep monitoring | -99 | -99 | -99 | -99 | -99 | Get all permissions | -99 | -99 | I don't know | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | quoted | null | No | -99 | -99 |
97 | Completed (31) | 1,435 | Computer Science | -99 | M.Sc. in Computer Science | Ph.D. in Informatics | Coursera | -99 | Brazil | More than 2,000 employees | Other, which one? | Machine Learning Engineer | 15 | 12 | 16 | 6 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | quoted | Denoising, Image Translation, Pose Estimation, Forecasting | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Very Complex | Complex | Easy | Complex | Neutral | Neutral | Neutral | 30 | 10 | 5 | 25 | 5 | 20 | 5 | Understand the business | -99 | -99 | Where to get the data | -99 | -99 | Handle data errors | -99 | -99 | Find the best model | -99 | -99 | Appropriate metrics | -99 | -99 | Scalability | -99 | -99 | Set triggers | -99 | -99 | -99 | -99 | -99 | Appropriate metrics | Handle data errors | Scalability | Sometimes | 20 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Machine Learning Engineer | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | quoted | MLOps nv2 pipeline | not quoted | 10 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | AutoKeras | -99 |
98 | Completed (31) | 1,465 | Mathematics, Physics | Engineering, Mechanics, Psychology | -99 | Mechanics and Numerical Simulation | Data Science Certification | -99 | France | 251-500 employees | Data Scientist | -99 | 4 | 4 | 12 | 10 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Pricing | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Groovy, Postgresql | quoted | define the best prices to increase total revenue / estimate sales volume based of price | not quoted | -99 | not quoted | -99 | quoted | cluster the customers by pricing-behaviour typology | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Neutral | High Relevance | High Relevance | Neutral | High Relevance | High Relevance | Complex | Easy | Easy | Complex | Easy | Very Complex | Complex | 35 | 5 | 10 | 30 | 5 | 10 | 5 | have the same vocabulary than the customer | the customer just wants a better prediction | we don't know about hidden/obvious for the customer requirements | customer isn't able to let them access to the data | prove our data infrastructure security | -99 | very rare formatting problem, that we don't have in our sample | merge tables that haven't shared keys | discover new needed preprocessing with the first bad results | how to choose the best model | -99 | -99 | decide what to check to decide if a result is good or bad? | provide relevant outputs | decide if we should override the ML evaluation in some cases | make a data scientist code a production code (readability, maintanability...) | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | we don't know about hidden/obvious for the customer requirements | the customer just wants a better prediction | decide if we should override the ML evaluation in some cases | Frequently | 70 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 5 | not quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://ww2.unipark.de/uc/seml/?fbclid=IwAR0THRpDx-5mwmS4z_YPz_HH60y3golOiVrWeE-mArj7p_JXl-0BzXcfy00 |
198 | Completed (31) | 889 | Economics | -99 | Electrical Engineering | Computer Science | -99 | -99 | United Kingdom | 11-50 employees | Other, which one? | CEO and CTO | 2 | 8 | 10 | 5 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | Human Resources | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | C# | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | Neutral | High Relevance | Extremely Relevant | Neutral | High Relevance | Complex | Complex | Neutral | Neutral | Complex | Neutral | Complex | 25 | 25 | 10 | 20 | 5 | 10 | 5 | Lack of understanding of Business of the limitations of Data Science | -99 | -99 | Available data not matching Business Reqs | -99 | -99 | Excess of feature engineering | -99 | -99 | Limited repertoire of the Data Scientist | -99 | -99 | Using wrong or none validation procedure | -99 | -99 | Lack of understanding of Data Science from Soft Engineers | -99 | -99 | Lack of adequate monitoring interfaces | -99 | -99 | -99 | -99 | -99 | Available data not matching Business Reqs | Limited repertoire of the Data Scientist | Lack of understanding of Data Science from Soft Engineers | Frequently | 80 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 25 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
104 | Completed (31) | 933 | -99 | -99 | M.Sc. Engineering Physics | Tecn.Lic. in Software Engineering | BTH Masters Course in ML, Combient course in Applied Data Science | -99 | Sweden | More than 2,000 employees | Solution Architect | -99 | 25 | 1 | 3 | 0 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Easy | Easy | Easy | Complex | I don't know | I don't know | 20 | 15 | 15 | 10 | 20 | 10 | 10 | Figuring out what need is to be met | Choosing a reliable (relevant) model evaluation metric | -99 | Finding data sources | Incomplete/conflicting data | -99 | Incomplete/conflicting data | -99 | -99 | Choosing relevant models to start to evaluate | -99 | -99 | What evaluation metric is relevent for my problem(s)? | Hyperparameter tuning | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Figuring out what need is to be met | Incomplete/conflicting data | -99 | Frequently | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | quoted | I have not really deployed any ML system in production yet | No | -99 | -99 |
107 | Completed (31) | 471 | Computer Science | Infosec Management | -99 | -99 | -99 | -99 | Brazil | 251-500 employees | Test Manager / Tester | -99 | 14 | 4 | 10 | 6 | 11-20 members | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | High Relevance | Very Complex | Neutral | Very Complex | Neutral | Neutral | Neutral | Complex | 25 | 5 | 20 | 15 | 10 | 10 | 15 | Accurate problem identification | Misunderstanding requirements | Solutions to problems that don't exist | Data selection | Obtaining access to data sources | -99 | Merging data from multiple sources and formats | Guaranteeing data quality | -99 | Obtaining sufficient test data for training | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Correct identification of results | -99 | -99 | -99 | -99 | -99 | Misunderstanding requirements | Obtaining access to data sources | Guaranteeing data quality | Frequently | 100 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 40 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
113 | Completed (31) | 2,473 | Computer Science | -99 | Autonomous intelligent systems | -99 | Certified Data scientist specialized in data analytics | -99 | 0 | More than 2,000 employees | Project Lead / Project Manager | -99 | 7 | 5 | 3 | 2 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | Low Relevance | Neutral | Complex | Neutral | Complex | Complex | Complex | Easy | Easy | 20 | 15 | 25 | 15 | 10 | 10 | 5 | Customer not aware of ML limitations leading to faulty requirements | -99 | -99 | Gdpr | Too few samples | Train/test distribution not matching real world data distribution | Data imbalance | Falsely labeled data | -99 | Finding the best model / approach | Model accuracy optimization | Model Speed optimization | Interpreting the metric correctly and knowing what it means exactly | Choosing the right metric | -99 | Performance (samples per second) | Hardware requirements / cost | -99 | Finding real world data issues | -99 | -99 | Expectation management with future users | -99 | -99 | To few samples / data | Customer not aware of ML limitations leading to faulty requirements | Train/test distribution not matching real world data distribution | Frequently | 60 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Product owner | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
116 | Completed (31) | 1,303 | Electrical and Electronics Engineering | -99 | Electrical Engineering | Computer Science | -99 | -99 | Brazil | 1,001-2,000 employees | Other, which one? | Professor | 30 | 30 | 70 | 25 | 6-10 members | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Electricity Sector | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | Load and demand forecasting, tax forecasting | quoted | fraude detection, fault classification, equipment diagnosis, classification of diseases | not quoted | -99 | quoted | fraud detection, segmentation of seismic data | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Fuzzy Logic, Evolutionary Computation | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Neutral | Complex | Easy | Complex | Neutral | 10 | 15 | 25 | 30 | 10 | 5 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 10 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | We develop our own Automated Machine Learning models | -99 |
117 | Completed (31) | 2,192 | Production Engineering | Business Inteligence | -99 | -99 | Coursera Data Science Professional Certificate | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 1 | 1 | 2 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Compliance | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | classification of news according to relevance | quoted | association of news according to subject | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Very Complex | Complex | Very Complex | Complex | Complex | Complex | Complex | 30 | 10 | 30 | 10 | 5 | 10 | 5 | Not familiar with subject | Client with no time | Confusing ideas from client | Lack of knowledge of different data bases available | -99 | -99 | Poor data quality | Same attributes in different data bases | -99 | Which models to use | -99 | -99 | Which metrics to use | Result below expected | -99 | No Infrastructure available | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Result below expected | Confusing ideas from client | No Infrastructure available | Frequently | 80 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
123 | Completed (31) | 1,224 | Industrial Pharmacy | Data Science specialization | -99 | -99 | -99 | Green Belt Certification | Brazil | More than 2,000 employees | Business Analyst | -99 | 0 | 0 | 1 | 1 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Using classification to identify clothes and classify them all | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Complex | Very Complex | Complex | Complex | Very Complex | Complex | Neutral | 40 | 10 | 30 | 5 | 5 | 5 | 5 | lack of experience of the people envolved | lack of knowledge of the business | lack of time to spent on solving problems techniques | lack of (good) data | -99 | -99 | lack of time | lack of patience to clean data | -99 | lack of time | -99 | -99 | lack of knowledge of the business | -99 | -99 | lack of time | -99 | -99 | lack of patience | lack of interest in keep monitoring and improving | -99 | -99 | -99 | -99 | lack of knowledge of the business | lack of (good) data | lack of time | Sometimes | 50 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | quoted | not quoted | quoted | not quoted | -99 | No | -99 | -99 |
128 | Completed (31) | 815 | Statistics | null | Biostatistics | Statistics, currently in progress | null | -99 | Brazil | 51-250 employees | Data Scientist | -99 | 3 | 3 | 3 | 2 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Complex | Complex | Complex | Very Complex | Very Complex | Complex | Complex | 15 | 15 | 25 | 20 | 10 | 10 | 5 | Data collection | Quality data | Standard the data | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 59 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 36 | quoted | quoted | quoted | quoted | not quoted | -99 | No | -99 | http://lnkd.in/ |
132 | Completed (31) | 1,149 | Computer Science | -99 | Computer Science | -99 | -99 | -99 | Brazil | 11-50 employees | Requirements Engineer | -99 | 4 | 2 | 1 | 1 | 11-20 members | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | gas emission | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Very Complex | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | 5 | 5 | 5 | 70 | 5 | 5 | 5 | low end-user participation | the time dedicated to this step is reduced | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | low end-user participation | the time dedicated to this step (requirements) is reduced | -99 | Sometimes | 40 | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | quoted | meetings with stakeholders | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | -99 | quoted | -99 | not quoted | 100 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | -99 | -99 |
154 | Completed (31) | 1,271 | Computer Science | -99 | -99 | -99 | -99 | Database specialization | Brazil | 11-50 employees | Developer | -99 | 7 | 0 | 0 | 0 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | null | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | Complex | Very Complex | Complex | Complex | Complex | Neutral | Complex | 10 | 20 | 10 | 30 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | I don't know | -77 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | null | not quoted | not quoted | not quoted | quoted | null | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | quoted | null | 0 | -99 | -99 |
133 | Completed (31) | 856 | Business Informatics | Business Engineering | Business Informatics | -99 | -99 | -99 | Germany | More than 2,000 employees | Test Manager / Tester | -99 | 8 | 1 | 1 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | I don't know | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Automotive | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Neutral | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Very Complex | Neutral | Complex | Complex | Complex | Complex | 15 | 20 | 10 | 15 | 15 | 15 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 60 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Stream Lead Application Development, Requirements Engineering | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
135 | Completed (31) | 1,637 | Computer Science | Marketing, Finances | M.Sc. in Computer Science | Ph.D. in Computer Science | -99 | -99 | Brazil | 1,001-2,000 employees | Other, which one? | Lecturer | 20 | 13 | 6 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Prediction of trends, calls and puts for investiment management | quoted | Classification of biometric signals for health sensors | quoted | cause and effect association applied in traffic network flows to determine congestion behavior and base speed profiles | quoted | User preferences profiling based on user behaviors, relationships and item evaluation | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | Markovian Decision Processes and Uncertainty | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | Complex | Very Complex | Neutral | Easy | Neutral | Neutral | Complex | 15 | 30 | 10 | 20 | 10 | 5 | 10 | determine the limits of the problem | find sources and references that determine the correct direction to take | -99 | amount of data to be collected | data accuracy | -99 | processing time | -99 | -99 | processing time | -99 | -99 | benchmark selection | -99 | -99 | -99 | -99 | -99 | dynamic environments | online problems | -99 | -99 | -99 | -99 | quality of data collection | choice of suitable model | partial data collection (absence of dimensions necessary for the problem) | Frequently | 35 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | quoted | not quoted | -99 | No | -99 | -99 |
147 | Completed (31) | 2,503 | Computer Engineering | Nenhuma | Nenhuma | Nenhuma | HCIA Machine Learning, HCIA Machine Learning NV2 | -99 | Brazil | 1-10 employees | Other, which one? | Bolsista | 0 | 1 | 2 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Sistemas de internet das coisas | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Previsão de fraude bancário | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | Complex | Neutral | Complex | Easy | Very Complex | Neutral | Neutral | 5 | 10 | 20 | 5 | 40 | 10 | 10 | Identificar o tipo de abordagem | -99 | -99 | Dados mau formados | Dados irrelevantes | -99 | Descobrir melhor tecnica de pre-processamento para o data frame me questão | -99 | -99 | -99 | -99 | -99 | Descobrir melhor metríca para o problema em questão | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Descobrir melhor tecnica de pre-processamento para o data frame em questão | Identificar o tipo de abordagem | Descobrir melhor metríca para o problema em questão | Frequently | 35 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
148 | Completed (31) | 830 | Mathematics | Data Science specialization | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 1 | 2 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Forecasting of company sales | not quoted | -99 | not quoted | -99 | quoted | Segmentation of stores and customers | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | 0 | High Relevance | Very Complex | Neutral | Neutral | Complex | Complex | Very Complex | Complex | 5 | 10 | 35 | 30 | 10 | 10 | 0 | Balancing stakeholders' wishes with what is possible | -99 | -99 | Aggregating data from separate sources | Validation | -99 | Wrong inputs | -99 | -99 | Feature engineering | -99 | -99 | Choosing the most suitable metric | -99 | -99 | Deploying the pipelines when the company doesn't have the infrastructure | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Choosing the most suitable metric | Deploying the pipelines when the company doesn't have the infrastructure | -99 | Sometimes | 20 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 100 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
157 | Completed (31) | 1,035 | Mechatronics Engineering | Data Science | Engineering and Management of Complex Systems - Machine Learning | Ph.D. in Astrophysics | Coursera DeepLearning certification | -99 | France | More than 2,000 employees | Other, which one? | PhD student DataScience applied in astrophysics | 4 | 3 | 7 | 4 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Physics | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | quoted | Generation | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Multi Armed Bandit | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Complex | Easy | Very Easy | Neutral | Complex | Neutral | 15 | 15 | 25 | 10 | 10 | 15 | 10 | -99 | -99 | -99 | -99 | x | -99 | -99 | -99 | x | -99 | -99 | -99 | -99 | -99 | -99 | x | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data Pre-Processing | Model Deployment | Data Collection | Sometimes | 40 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Jira | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | Bento ML, Vertex AI (Google), MLFlow | not quoted | 100 | not quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | H2O | https://ww2.unipark.de/uc/seml/ |
155 | Completed (31) | 1,302 | Computer Science | -99 | M.Sc. in Computer Science | Ph.D. in Computer Science | -99 | -99 | Brazil | 11-50 employees | Project Lead / Project Manager | -99 | 5 | 2 | 6 | 4 | 6-10 members | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Very Complex | Very Complex | Very Complex | Complex | Complex | Complex | Complex | 30 | 20 | 15 | 15 | 10 | 5 | 5 | interaction with experts | Documenting requirements | -99 | data understanding | -99 | -99 | interaction with experts | -99 | -99 | model selection | Aligning requirements with data quality issues | -99 | performance analysis | Selecting proper evaluation metrics | -99 | customer environment | -99 | -99 | performance analysis | -99 | -99 | -99 | -99 | -99 | interaction with experts | data understanding | performance analysis | Sometimes | 50 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | quoted | Microsoft Azure Continuous Integration Pipeline | not quoted | 50 | not quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Microsoft Azure Continuous Integration Pipeline | -99 |
161 | Completed (31) | 170 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://t.co/ |
168 | Completed (31) | 1,904 | Computer Engineering | -99 | -99 | -99 | Google introduction to ML | -99 | Turkey | 51-250 employees | Solution Architect | -99 | 15 | 1 | 1 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Manufacturing | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | finding bottlenecks in production line | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | High Relevance | Extremely Relevant | High Relevance | Very Complex | Very Complex | Complex | Neutral | Neutral | Neutral | Easy | 25 | 25 | 7 | 15 | 15 | 7 | 6 | finding relevant usecase | Most of the employees are not interested in the subject. | to persuade top management about costs | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | finding relevant usecase | Most of the employees are not interested in the subject. | to persuade top management about costs | Sometimes | 50 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | quoted | quoted | not quoted | -99 | No | -99 | -99 |
170 | Completed (31) | 1,088 | Industrial Engineering | -99 | Computer Science | -99 | Courses | -99 | Turkey | 11-50 employees | Data Scientist | -99 | 5 | 5 | 10 | 10 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Easy | Neutral | Neutral | Neutral | Complex | Neutral | 10 | 10 | 20 | 35 | 10 | 15 | 0 | Problem owners don't understand how ML provide benefits to their business. | -99 | -99 | Generally it is time consuming to collect all relevant data. | -99 | -99 | Feature generation is difficult and require business domain knowledge. | -99 | -99 | I spent most of time to develop model and ensemble methods of those models. | -99 | -99 | Which metric to use with business department is time consuming. | -99 | -99 | If team member is not software oriented, it is difficult and demotivating. | -99 | -99 | It is difficult to track performance of each model. | -99 | -99 | Updating of current models | -99 | -99 | Model deployement | lack of business domain knowledge or problem identification | -99 | Sometimes | 40 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | We developed internal pipeline to monitor and deploy our models easily. | not quoted | 80 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
215 | Completed (31) | 1,026 | Computer Science | Computer Engineering, Software Engineering | Computer Science | Computer Science | -99 | -99 | Turkey | 1-10 employees | Project Lead / Project Manager | -99 | 22 | 8 | 18 | 10 | 6-10 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | .Net Core, C# | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Very Complex | Complex | Complex | Neutral | Complex | Complex | Complex | 20 | 20 | 20 | 5 | 10 | 15 | 10 | Having no common data vocabulary | Having no clear functional requirements | -99 | Different data collection protocols for IoT devices | -99 | -99 | Lack of business understanding | -99 | -99 | ML Engineers tend to build complex models | -99 | -99 | Blacbox models and their lack of explainability | -99 | -99 | No data engineer in the system | -99 | -99 | Lack of simple visualization | -99 | -99 | -99 | -99 | -99 | Lack of business understanding | Blacbox models and their lack of explainability | No data engineer in the system | Frequently | 25 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
176 | Completed (31) | 1,274 | Economics | Data Science specialization | M.SC. in Business Administration | -99 | Google Professional ML Engineer, Google Professional Data Engineer | -99 | Brazil | 51-250 employees | Data Scientist | -99 | 3 | 2 | 4 | 2 | 6-10 members | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Estimating the sales | quoted | Estimation of Client's score, estimation of probability of the clients situation | quoted | NLP | quoted | Segmentation of Clients base | quoted | Computer Vision | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | High Relevance | High Relevance | Complex | Neutral | Neutral | Neutral | Neutral | Complex | Very Complex | 25 | 20 | 15 | 7 | 12 | 15 | 6 | Clients don't know what ML can Do | Requirements too broad | Lack of understanding that ML sometimes does not delivers | Lack of a main unified source | Data Silos inside the organization | Lack of Data Stewardship | Data Management | -99 | -99 | Cost of Training | Cost of Hyperparameter Tunning | -99 | -99 | -99 | -99 | Lack of metadata of the training task | Complexity in integrating with the CI/CD pipelines | Bad practices in software Engineering | Lack of Observability Tools | Lack of personnel | -99 | -99 | -99 | -99 | Requirements too broad | Data Silos inside the organization | Lack of personnel | Frequently | 75 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | Architects, Tech Leads and managers | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 5 | quoted | quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | AutoKeras, AutoML in Vertex AI | -99 |
182 | Completed (31) | 1,514 | -99 | Data Science | -99 | -99 | -99 | -99 | Brazil | 251-500 employees | Other, which one? | Maintenance Engineer | 2 | 1 | 5 | 0 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Membrane saturation prediction, | quoted | Classification of healthy equipment | not quoted | -99 | quoted | Clustering of equipment conditions | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Complex | Complex | Very Complex | Complex | Complex | Complex | 15 | 20 | 30 | 15 | 10 | 5 | 5 | Not knowing what to get | Not knowing what benefits have | Not knowing where to attack | Data access | Small amount of data | Data standardization | Poor data quality | Transformation issues | Difference of sampling | Type of model | Time to perform | Fine tuning | Accuracy used | Specialist not assessing | Detailing | Not applicable to me | Not applicable to me | Not applicable to me | Constant training requirement | Too narrow training | Too much requirements | -99 | -99 | -99 | Problem not clearly stated | Time to perform the project | Constant training requirement | Sometimes | 50 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | not quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
195 | Completed (31) | 1,607 | Computer System Management | Project Management Specialization | -99 | -99 | -99 | -99 | Colombia | 11-50 employees | Other, which one? | Data Engineer | 5 | 1 | 2 | 1 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | Extremely Relevant | Neutral | High Relevance | Low Relevance | Low Relevance | Very Complex | Neutral | Complex | Easy | Neutral | Easy | I don't know | 30 | 20 | 30 | 5 | 5 | 5 | 5 | Los clientes no conocen muy bien lo que quieren o esperan de un sistema de ML. Son contradictorios sus puntos de vista sobre ML. | La trazabilidad de los requerimientos es muy pobre. Los cambios no son documentados. No se especifican muy bien los componentes. | -99 | la integración de datos es un problema que no es fácil de abordar | -99 | -99 | Las versiones de los dataset no se gestionan, raramente se hace esto. Eso muchas vezes ocasiona problema con los dados (diferencia entre datasets que causa confusión) | -99 | -99 | -99 | -99 | -99 | El entendimiento de las métricas de ML no es simple. Tiene una base estadística que no es fácil de entender | Para los clientes no es fácil decidir si un modelo es bueno o malo solo fijándose en la precisión o accuracy del modelo. | -99 | -99 | -99 | -99 | No es una tarea que comúnmente se haga y debería serlo. | -99 | -99 | -99 | -99 | -99 | Problem Understanding and requirements | Data Preprocessing | -99 | Frequently | 50 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | La trazabilidad de los requerimientos | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
196 | Completed (31) | 858 | Civil Engineering | Data Science and Statistics Computacional | Eletrical Engennering | student | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | -99 | 4 | -99 | -99 | 0 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Not Relevant at All | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Complex | Complex | Neutral | Complex | Complex | Complex | 25 | 30 | 15 | 10 | 10 | 5 | 5 | list | -99 | -99 | data set | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 70 | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | 80 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
207 | Completed (31) | 1,403 | Electrical and Electronics Engineering | -99 | M.Sc. in Electrical&Electronics Eng. | - | - | - | Turkey | More than 2,000 employees | Developer | -99 | 24 | 5 | 1 | 1 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | to classify modulation scheme of communication signal | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Very Complex | Very Complex | Very Complex | Complex | Complex | Complex | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Framework version changes | Model conversions | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 50 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | System Engineer | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
221 | Completed (31) | 1,354 | -99 | -99 | -99 | Ph.D in Computer Science | -99 | -99 | New Zealand | More than 2,000 employees | Developer | -99 | 10 | 3 | 2 | 5 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Text classification | not quoted | -99 | quoted | Group texts | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Neutral | Neutral | Complex | Neutral | Complex | Neutral | 30 | 10 | 5 | 30 | 10 | 10 | 5 | Identify the key difficulties of a approach | Find the relevant research | Find out the innovative concept | Data source | Access to the data | data privacy | Complicated Texts containing stranger chars | Different forms of data | -99 | Find the relevant model | Build the theoretical parts of the model | -99 | Find the useful metric | Find the standard dataset for evaluation | Find the optimal hyper-parameters | Computation time and power | Transfer the model | -99 | Postential to re-train the model | -99 | -99 | -99 | -99 | -99 | Identify the key difficulties | Data source | Transfer the model to other kinds of problems. | Frequently | 80 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://t.co/z2DSUht1bO |
222 | Completed (31) | 811 | Computer Science | -99 | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Project Lead / Project Manager | -99 | 2 | 2 | 4 | 2 | 11-20 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | Easy | Neutral | Complex | Complex | Complex | Neutral | Neutral | 20 | 20 | 15 | 15 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | Data Collection | Model Creation and Training | Sometimes | 25 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
226 | Completed (31) | 1,000 | Electrical and Electronics Engineering | Project Management Specialization | MBA | -99 | Artificial Intelligence | Asian Institute of Management | -99 | Turkey | 11-50 employees | Project Lead / Project Manager | -99 | 5 | 4 | 2 | 1 | 11-20 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | mobile apps | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | prediction of eCPM, eCPM means estimated cost per mille which uses in advertising for mobile and web applications. | quoted | using classification of app users according to their shopping behavior | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | Neutral | High Relevance | Neutral | Neutral | Neutral | Very Complex | Complex | Very Complex | Very Complex | Very Complex | Complex | Neutral | 0 | 30 | 20 | 25 | 10 | 10 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Rarely | 20 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://ww2.unipark.de/uc/seml/ |
227 | Completed (31) | 1,146 | Computer Engineering | Software Engineering specialization | M.Sc. in Computer Engineering | Ph.D. in Computer Science | -99 | -99 | Finland | 1,001-2,000 employees | Data Scientist | -99 | 6 | 3 | 4 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Neutral | Neutral | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Neutral | Easy | Easy | Very Complex | Very Complex | Complex | 10 | 10 | 10 | 10 | 20 | 30 | 10 | Unclear requirements | Unclear expectations | Communication issues with stakeholders | Data is not available | Data is not usable/dirty | -99 | Format conversion | Schema structuring/destructuring | -99 | Model selection without evaluation | -99 | -99 | Lack of labeled data | Lack of continuous feedback from stakeholders | -99 | Model scalability | Model availability | Synchronization issues | Monitoring added as second thought | -99 | -99 | -99 | -99 | -99 | Unclear requirements | Lack of continuous feedback from stakeholders | Model scalability | Frequently | 30 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
228 | Completed (31) | 1,672 | Computer Science | Artificial Intelligence | M.Sc. in Artificial Intelligence | Computer Science | -99 | -99 | Italy | More than 2,000 employees | Data Scientist | -99 | 10 | 5 | 5 | 3 | 6-10 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Complex | Very Complex | Neutral | Complex | Complex | Very Complex | Neutral | 20 | 30 | 20 | 15 | 10 | 5 | 0 | communication | setting goals | technical disalignment | data availability | data format | -99 | lack of resources for a specific language | -99 | -99 | lack of training data | need of large computational resources | -99 | lack of test set | -99 | -99 | deal with complex interfaces | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | data availability | need of large computational resources | -99 | Sometimes | 60 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
235 | Completed (31) | 825 | -99 | -99 | -99 | Information Technology | -99 | -99 | Austria | 501-1,000 employees | Other, which one? | technical director | 8 | 8 | 5 | 4 | 1-5 members | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | 0 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | 0 | Very Complex | Very Complex | Very Complex | Neutral | Complex | Complex | 10 | 20 | 20 | 20 | 10 | 10 | 10 | requirements are not clear | -99 | -99 | lack of data | -99 | -99 | low quality | -99 | -99 | time consuming | -99 | -99 | defining the right metric | -99 | -99 | CI/CD | -99 | -99 | accurate confidence measure | -99 | -99 | -99 | -99 | -99 | Model Monitoring | Data Collection | Model Creation and Training | Frequently | 50 | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | Azure Devops & Kubernetes | not quoted | 50 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
242 | Completed (31) | 3,246 | Computer Engineering | Computer Vision | -99 | -99 | -99 | -99 | Turkey | 251-500 employees | Developer | -99 | 3 | 2 | 3 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Sport | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | classification for facial expression recognition, human detection, ball detection | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Extremely Relevant | High Relevance | High Relevance | Complex | Neutral | Neutral | Neutral | Complex | Complex | Complex | 20 | 20 | 10 | 10 | 10 | 10 | 20 | Applying DL for all problems. The problem should be analysed well and related solutions will be tried. | Expecting ML solutions work for all environments. | -99 | Data Annotation Quality should measured and maintained | All scenarios should be included. | Version control should be done and updates should be reported | Building a pipeline | Version control should be done and updates should be reported | -99 | Building a pipeline | Version control should be done and updates should be reported | -99 | Building a pipeline | Choosing right success metrics | Version control should be done and updates should be reported | Keeping stable model success on deployment environment | Version control should be done and updates should be reported | -99 | Extracting meaningful graphs etc. | Version control should be done and updates should be reported | -99 | -99 | -99 | -99 | Expecting ML solutions work for all environments. | All scenarios should be included. | Version control should be done and updates should be reported | Sometimes | 100 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | quoted | quoted | not quoted | -99 | No | -99 | -99 |
236 | Completed (31) | 1,266 | Computer Science | Computer Vision | Computer Science | -99 | -99 | -99 | Turkey | 51-250 employees | Project Lead / Project Manager | -99 | 5 | 5 | 6 | 5 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Sports | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Object detection, Image Segmentation, Keypoint Estimation, Variational Auto Encoders | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Neutral | Extremely Relevant | High Relevance | Complex | Complex | Neutral | Easy | Easy | Neutral | Neutral | 25 | 30 | 5 | 5 | 5 | 20 | 10 | Segmenting problem into smaller simpler problems | -99 | -99 | Finding Data | Time necessary to label the data | Wrong annotations from annotators, -fixing the annotations- | Keeping the data organized | Keeping the domain of data as small as possible to be able to run smaller models | -99 | Manipulating the models to be smaller in order to gain inference speed | Creating convertable models(TVM, tensorRT, RKNN, CoreML) for deployment | -99 | Choosing the right metrics for the purpose of a single problem the model is aimed to solve | -99 | -99 | Using frameworks to deploy developed models in C++ applications | Manipulating models to be convertable | -99 | Identifying the point of failure in an application-whether its the developed model or its deplotment implementation bugs- | -99 | -99 | -99 | -99 | -99 | Segmenting problem into smaller simpler problems | Choosing the right metrics for the purpose of a single problem the model is aimed to solve | -99 | Sometimes | 50 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | We have our own pipelines, its currently being documented as an orientation guide | not quoted | 80 | quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
238 | Completed (31) | 1,433 | Electrical and Electronics Engineering | Computer Vision | M.Sc. in Cognitive Science | -99 | NVIDIA - Deep Learning for Computer Vision, NVIDIA - Deep Learning for Multiple Data Types, Coursera - Machine Learning from Andrew NG | -99 | Turkey | 51-250 employees | Developer | -99 | 5 | 4 | 6 | 5 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Sports | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Instance Segmentation, Human Pose Estimation | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Very Complex | Easy | Neutral | Easy | Very Complex | Neutral | 15 | 5 | 40 | 10 | 10 | 15 | 5 | model size and its speed on edge devices | better solutions for problem | -99 | -99 | -99 | -99 | Unorganized data | Different data format | Data size | Finding most optimal model for the case | Overfitting | low accuracy | -99 | -99 | -99 | C++ environment is kinda complex with respect to python | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | model size and its speed on edge devices | low accuracy | -99 | Always | 80 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | CEO | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | Git version control with proper documentation for both deployment and development side | not quoted | 20 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
237 | Completed (31) | 683 | Electrical and Electronics Engineering | Computer Vision | Electronics | -99 | -99 | -99 | Turkey | 51-250 employees | Developer | -99 | 3 | 1 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Sports | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Low Relevance | Low Relevance | Low Relevance | Neutral | Low Relevance | Complex | Neutral | Easy | Easy | Easy | Easy | Easy | 50 | 20 | 2 | 10 | 2 | 10 | 6 | it requires to master the whole ML topics | -99 | -99 | hard to find | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | Data Collection | Other problems | Sometimes | 20 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
240 | Completed (31) | 1,101 | Industrial Engineering | Data Science | MSc in Big Data and Business Analytics | - | Datacamp Data Scientist Path, Datacamp Data Analyst Path, Datacamp Machine Learning Engineer Path | -99 | Turkey | 51-250 employees | Data Scientist | -99 | 3 | 3 | 10 | 4 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Sports, Production | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | estimate spare parts consumption in a factory, estimate machine stop time(predictive maintenance) | quoted | classify if the product is damaged or not(computer vision) | not quoted | -99 | quoted | customer cluster | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | Very Complex | Neutral | Complex | Easy | Very Complex | Neutral | Complex | 30 | 20 | 25 | 5 | 10 | 5 | 5 | No clear definition | No data provided | -99 | Different type of databases(sql - noSql) | Create a survey if necessary | -99 | Dirty data | Wrong Data(wrong measurement by sensors) | -99 | -99 | -99 | -99 | Find the best error metric | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | managers who want quick and reliable solutions | -99 | -99 | No clear definition | No data provided | Wrong Data(wrong measurement by sensors) | Frequently | 40 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
277 | Completed (31) | 725 | Computer Engineering | -99 | Computer Engineering | Computer Engineering | -99 | -99 | Ireland | 51-250 employees | Data Scientist | -99 | 20 | 20 | 20 | 5 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Neutral | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Neutral | Easy | Complex | Very Complex | Neutral | Neutral | Very Complex | Neutral | 10 | 20 | 30 | 10 | 10 | 10 | 10 | -99 | -99 | -99 | Customer systems | Scattered unstructured data | Customer not knowing the data | Unstructured data | Learning data relations | Data size | Target deployment environment | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Customer not knowing the data | Scattered unstructured data | Target deployment environment | Rarely | 20 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
252 | Completed (31) | 1,958 | Computer Science | Big Data, AI, HCI, Computational Math, Distributed Systems, MultiAgent Systems | M.Sc. in Computer Science | Ph.D. in Computer Science and Mathematics (AI, Machine Learning, Data Mining) | AWS Certified Machine Learning - Specialty | -99 | Italy | More than 2,000 employees | Data Scientist | -99 | 12 | 7 | 15 | 3 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | Inductive Logic Programming, Non-Negative Matrix Factorization, PCA, SVD, | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Complex | Very Complex | Complex | Easy | Neutral | Neutral | 10 | 15 | 40 | 15 | 10 | 5 | 5 | problem not clear | requirements not clear | both not clear | where do we find data? | where do we collect data? | how do we collect data? | datetime problems | anomalies handling | outliers handling | which model do we select? | hyperparameters tuning handling | -99 | which cross-validation methdo do we select? | ECDF charts | -99 | is the model still running? | -99 | -99 | A/B Testing | -99 | -99 | -99 | -99 | -99 | not providing the prediction in time | -99 | -99 | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | quoted | simulating system's behavior | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 100 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
254 | Completed (31) | 566 | -99 | Data Science specialization | -99 | -99 | AWS Machine Learning Specialty | -99 | Italy | 1,001-2,000 employees | Data Scientist | -99 | 5 | 2 | 3 | 1 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Energia imbalance | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Complex | Complex | Complex | Neutral | Neutral | Easy | Easy | 20 | 30 | 30 | 10 | 10 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | AWS Sagemaker MLOPS | not quoted | 100 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
257 | Completed (31) | 3,383 | Software Engineering | Data Science | -99 | -99 | -99 | -99 | Italy | More than 2,000 employees | Other, which one? | Both developer and data scientist | 0 | 0 | 2 | 0 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | Using text to predict intent and sentiment | not quoted | -99 | not quoted | -99 | quoted | Object detection for garbage identification | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | BERT, Yolo | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Neutral | Complex | Complex | Neutral | Easy | Neutral | Neutral | 10 | 15 | 35 | 20 | 15 | 5 | 0 | Understand the domain and user goals or expectations | Define an incorrect ML task | Define some standard of quality | Annotate data if possibile and necessary | -99 | -99 | Clean data from noise without remove outliers | -99 | -99 | -99 | -99 | -99 | Have bad score results when we test the model with the test set | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | 0 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 1 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | H2O | -99 |
258 | Completed (31) | 1,859 | Physics | Physics | Physics | -99 | AWS Machine Learning | -99 | Italy | More than 2,000 employees | Data Scientist | -99 | 6 | 6 | 2 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Very Complex | Neutral | Neutral | Neutral | Complex | Complex | 20 | 20 | 15 | 15 | 10 | 10 | 10 | small time to dedicate for this step | -99 | -99 | difficulty to find big datasets | -99 | -99 | managing a lot of errors in data | -99 | -99 | long time to find the best model | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem understanding and requirements | Data collection | Model creation and trianing | Sometimes | 60 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | not quoted | not quoted | not quoted | quoted | not quoted | -99 | No | -99 | -99 |
275 | Completed (31) | 2,875 | -99 | -99 | yes | -99 | -99 | -99 | Germany | 1-10 employees | Project Lead / Project Manager | -99 | 13 | 4 | 3 | 2 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | I don't know | 40 | 30 | 10 | 10 | 10 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://mail.google.com/ |
282 | Completed (31) | 1,785 | Computer Science | -99 | M,Sc. in Applied Computing | -99 | -99 | -99 | Brazil | 1-10 employees | Developer | -99 | 17 | 2 | 2 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Task List | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | Car Plate Detection, Car Detection, Face Detection | quoted | Face Recognition, Number Recognition | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | High Relevance | Extremely Relevant | Neutral | High Relevance | High Relevance | Extremely Relevant | Low Relevance | Complex | Easy | Very Complex | Very Complex | Neutral | Very Complex | Neutral | 10 | 10 | 10 | 25 | 10 | 25 | 10 | it's always a new solution | -99 | -99 | Only known issues have a database (Ex: Car detection) | -99 | -99 | many techniques to test | -99 | -99 | -99 | -99 | -99 | Find experts in the problem area | -99 | -99 | Requires a lot of development time | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | it's always a new solution | Requires a lot of development time | Many techniques to test | I don't know | 30 | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
283 | Completed (31) | 595 | Computer Science | CS, SE | Master in CS | -99 | -99 | BSc Economics, BSc Engineering | Sweden | More than 2,000 employees | Other, which one? | I am both Data Scientist and Software Engineer | 15 | 10 | 5 | 5 | 20-50 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | V model | Mostly traditional | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | Secret ones that can not be disclosed | Extremely Relevant | Neutral | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | Very Complex | 0 | Complex | Neutral | Neutral | Neutral | Neutral | 5 | 20 | 10 | 40 | 10 | 5 | 10 | Focus on symtoms rather than the problem | Not understanding the real needs but solution focus | cool over beneficial | Access | -99 | -99 | Heterogenicity | -99 | -99 | Good-enough vs. time spent | -99 | -99 | Hard to get money for | -99 | -99 | More focus on getting it to work than getting it to work well | -99 | -99 | Seldom done | -99 | -99 | -99 | -99 | -99 | Focus on symtoms rather than the problem | Not understanding the real needs but solution focus | cool over beneficial | Always | 80 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | No one | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | They were not really, invented | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | Custom in-house | -99 |
284 | Completed (31) | 1,232 | Computer Science | Data Science | -99 | -99 | Datacamp DS With Python Track | -99 | Brazil | 1,001-2,000 employees | Data Scientist | -99 | 4 | 4 | 6 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | I don't know | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | SQL | not quoted | -99 | quoted | Churn, Propensity models for sales, Antifraud | not quoted | -99 | quoted | Defining competition groups | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Complex | Complex | Complex | Complex | Complex | Complex | 15 | 25 | 40 | 10 | 10 | 0 | 0 | Communication with the Business Unit | Decentralised knowledge | -99 | Lack of data | Bias | Decentralised sources | Errors can compromise the whole project (leaking, ...) | Unstructured data | Feature building | Bias/ Variance tradeoff | Choosing the right algorithm for your infrastructure | Splitting the dataset | Choosing the right metric | Choosing Feature selection approach | Identifying overfit | Lack of knowledge on how to do this | -99 | -99 | Lack of knowledge on how to do this | -99 | -99 | People often won't see DS as science, removing the experimental point of view. Managers tend to get impatient if no good results come out of the experiment. | -99 | -99 | Model Deployment lack of know how | Data Collection lack of data | Managers seeing DS projects as traditional software engineering projects. | Frequently | 45 | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Pytorch | -99 |
286 | Completed (31) | 2,734 | Industrial Engineering | -99 | Data Science | -99 | Azure Machine Learning | -99 | Brazil | 251-500 employees | Data Scientist | -99 | 3 | 3 | 3 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Using predictions to estimate oil and grail | quoted | Using classification to estimate seismogram noise quality | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Low Relevance | High Relevance | Low Relevance | Complex | Complex | Very Complex | Neutral | Easy | Neutral | Neutral | 30 | 10 | 30 | 15 | 5 | 5 | 5 | Scheduling with Experts | Bad Problem definition | Bad Feature Selection | Authorization | Access restrictions(proxy and virtual machines) | Massive variety of systems | Bad Data structure | Custom and complex tasks | -99 | Bad Feature Selection | Data engineering | Model selection | Choose evaluation metric | -99 | -99 | Set up Infrastructure | -99 | -99 | Establish automatic model monitoring | -99 | -99 | Poor Quality Data | -99 | -99 | Bad problem definition | Bad Feature Selection | Poor quality data | Always | 80 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | not quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
290 | Completed (31) | 2,284 | Software Engineering | Software analyst | Software Engineering | -99 | -99 | -99 | Turkey | 1,001-2,000 employees | Business Analyst | -99 | 3 | 0 | 1 | 1 | 20-50 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Kamu Kurumu Projesi | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | .Net Core | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Very Complex | Neutral | Easy | Neutral | Very Complex | Complex | Complex | 10 | 15 | 20 | 20 | 10 | 15 | 10 | 3 | 2 | 1 | 2 | 3 | 1 | 3 | 2 | 1 | 3 | 2 | 1 | 2 | 3 | 1 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | Sometimes | 75 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
297 | Completed (31) | 3,674 | Actuarial Science | Data Science specialization | -99 | -99 | -99 | Actuarial and Financial Management specialization | Brazil | 1,001-2,000 employees | Data Scientist | -99 | 21 | 1 | 1 | 0 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | SAS | quoted | medical costs | not quoted | -99 | not quoted | -99 | quoted | Medical procedures, hospitalizations, medical costs | quoted | interoperability between health information systems | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | I don't know | High Relevance | Neutral | Easy | Complex | Complex | Complex | I don't know | Neutral | 10 | 3 | 70 | 10 | 5 | 0 | 2 | many meetings | -99 | -99 | choose tools | -99 | -99 | manage time for task | manage persons for task | -99 | choose parameters | -99 | -99 | reduced time to decide | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | manage time for task | reduced time to decide | -99 | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://mail.google.com/ |
298 | Completed (31) | 558 | Computer Engineering | Oracle DB Developer | -99 | -99 | -99 | -99 | Turkey | 251-500 employees | Developer | -99 | 4 | 0 | 0 | 0 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ |
301 | Completed (31) | 3,443 | Computer Science | Data Science specialization | -99 | -99 | Applied Data Science Camp | Oracle Database expert | Sweden | More than 2,000 employees | Data Scientist | -99 | 10 | 2 | 2 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | using prediction to forecast revenue. Using prediction to predict User experience. | not quoted | -99 | not quoted | -99 | quoted | -99 | quoted | Anomaly detection | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Prophet | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Very Complex | Very Complex | Very Complex | Neutral | Neutral | Neutral | Easy | 20 | 30 | 10 | 10 | 10 | 10 | 10 | poor quality business description | Assumption that all problems can be resolved in ML | -99 | Data location and distribution | data quality | lack of availability of large historical data | Data quality | compute resources | data understanding | compute resources | model selection | -99 | understanding the model output | -99 | -99 | continues Data streaming | infrastructure | Architecture | time dedication | -99 | -99 | -99 | -99 | -99 | Data Collection | infrastructure | Problem Understanding and Requirements | Frequently | 90 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Product owners. Customers | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
304 | Completed (31) | 637 | Telecommunications Engineering | -99 | Computer Science, Business Administration, IT Project Management | -99 | Microsoft certifications: Microsoft - DAT203.2x Principles of Machine Learning , DAT210x Programming with Python for Data Science | Ericsson Data Science Camp | Sweden | More than 2,000 employees | Other, which one? | Researcher | 4 | 3 | 2 | 1 | 11-20 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | predict QoS | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Very Complex | Very Complex | Very Complex | Complex | Complex | Neutral | Neutral | 30 | 20 | 20 | 10 | 10 | 5 | 5 | Poorly understood problem | Absence of stakeholders | -99 | Inefficient data collection | Bugs | -99 | Poorly understood problem | Insufficient data | -99 | Insufficient data | Insufficient data | -99 | Metrics are not linked to the problem | -99 | -99 | Poor communication between ML developers and software developers | -99 | -99 | Metrics are not linked to the problem | -99 | -99 | -99 | -99 | -99 | Poorly understood problem | Inefficient data collection | Insufficient data | Sometimes | 50 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
309 | Completed (31) | 1,263 | Mathematics | Software implementation project manager | Software engineering | -99 | Data science - Spark Academy | -99 | Iran | More than 2,000 employees | Project Lead / Project Manager | -99 | 15 | 1 | 1 | 1 | 11-20 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Energy management | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Fraud detection on energy management system | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | Low Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Easy | Neutral | Complex | Complex | Very Complex | Complex | Complex | 10 | 15 | 15 | 25 | 15 | 10 | 10 | understanding the financial impact | -99 | -99 | weak data rate sample | -99 | -99 | limited on embed system | -99 | -99 | documentation | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | 30 | -99 | -99 | Frequently | 60 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 90 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
314 | Completed (31) | 2,129 | Manufacturing Engineering | Data Science specialization | M.Sc. in Computer Science | -99 | -99 | -99 | Canada | 11-50 employees | Developer | -99 | 12 | 2 | 2 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Text message classification | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Neutral | Extremely Relevant | Neutral | Extremely Relevant | Very Complex | Neutral | Very Complex | Neutral | Very Complex | Neutral | Complex | 20 | 10 | 30 | 10 | 10 | 5 | 15 | Problem Understanding | -99 | -99 | -99 | -99 | -99 | Data Sanitization | Data Parsing | -99 | Machine Limitations | Getting reliable data | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem Understanding | Getting reliable data | Data Parsing | Sometimes | 75 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 15 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
315 | Completed (31) | 1,119 | Computer Science | Data Science specialization | M.Sc.. in Algorithms, Graph Theory | Ph.D. in Algorithms, Graph Theory | -99 | -99 | Germany | 1-10 employees | Other, which one? | Machine Learning Engineer | 20 | 5 | 15 | 4 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | Transformers | Extremely Relevant | High Relevance | High Relevance | Neutral | High Relevance | High Relevance | Extremely Relevant | Complex | Neutral | Neutral | Neutral | Complex | Complex | Very Complex | 25 | 25 | 5 | 5 | 10 | 20 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 100 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | Mainly tools provided by Clouds | not quoted | -77 | not quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-sklearn and those specific by Cloud providers | -99 |
316 | Completed (31) | 910 | Computer Science | -99 | -99 | -99 | -99 | -99 | Brazil | 11-50 employees | Developer | -99 | 2 | 1 | -99 | -99 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | I don't know | Extremely Relevant | Very Complex | Complex | Very Complex | I don't know | Complex | Neutral | Neutral | 20 | 10 | 15 | 15 | 20 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | totally unstructured data | variety of data sources | treat each specificity that comes up | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | I don't know | -77 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
317 | Completed (31) | 3,566 | Computer Science | Systems analysis with agile methods | M.Sc. in informatic | -99 | -99 | -99 | Canada | 11-50 employees | Solution Architect | -99 | 15 | 1 | 1 | 0 | 1-5 members | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | System aimed at information security of companies in general. | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Prediction to predict cyber attacks | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Semantic analysis | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Complex | Neutral | Neutral | Neutral | Neutral | 10 | 20 | 20 | 30 | 10 | 5 | 5 | absence of the client in the project | need to import the data into the development environment | data scientists not able to talk to the client | no data sample showing the problem | confidential data | -99 | need a lot of programming to do jobs that other tools already do | -99 | -99 | great diversity of algorithms that generate a high learning curve to choose the most suitable for the problem | -99 | -99 | -99 | -99 | -99 | need to deploy into production each new model | -99 | -99 | -99 | -99 | -99 | the company did not pre-search for ready-made automl tools and instead chose to hire developers to build systems and models from scratch (recreate the wheel). | -99 | -99 | no data sample showing the problem | need to import the data into the development environment | data scientists not able to talk to the client | Frequently | 80 | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
318 | Completed (31) | 2,477 | Information Systems | -99 | Teleinformatics Engineering | -99 | -99 | -99 | Canada | 11-50 employees | Other, which one? | Research Lead | 15 | 2 | 1 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Network security | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Complex | Neutral | Complex | Very Complex | Neutral | Complex | 10 | 5 | 20 | 20 | 20 | 10 | 15 | Wanting to use ML for one but not knowing how to specify exactly the problem | If you have a client, eventually he thinks that ML is like magic, that we develop something capable of solving all problems | Working with a client who has many secret goals and doesn't deliver on the real need | Lack of tagged data | No data | Customer has the data but does not trust to pass it on to you | Wanting to do preprocessing from scratch | Use in-development data that does not match deployment data | -99 | Choosing the right algorithm | Parameterize the algorithm properly | Lack of data diversity | Overfitting | An appropriate architecture to delineate well between an online and an offline model | -99 | An appropriate architecture to delineate well between an online and an offline model | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | If you have a client, eventually he thinks that ML is like magic, that we develop something capable of solving all problems | Lack of data diversity | Wanting to do preprocessing from scratch | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
319 | Completed (31) | 761 | Computer Engineering | Product Management | MBA | -99 | -99 | ISTQB, CBAP | Turkey | 51-250 employees | Project Lead / Project Manager | -99 | 9 | -99 | 1 | 10 | 11-20 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | SPORTS | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Neutral | Neutral | Complex | Neutral | Complex | Complex | 5 | 10 | 20 | 20 | 20 | 15 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Always | 30 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Reporting via Jira & Confluence | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 90 | quoted | quoted | quoted | quoted | not quoted | -99 | No | -99 | -99 |
322 | Completed (31) | 618 | Computer Science | Big Data | -99 | -99 | -99 | -99 | Turkey | 11-50 employees | Solution Architect | -99 | 11 | 6 | 15 | 15 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Extremely Relevant | High Relevance | Complex | Easy | Easy | Complex | Complex | Neutral | Neutral | 25 | 15 | 5 | 20 | 5 | 20 | 10 | Business know how | -99 | -99 | Data Access | Data Understanding | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Business interaction | -99 | -99 | Integration | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Business know how | Business interaction | Integration | Frequently | 50 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | In some project we are using Dataiku as MLOps platform. In some, we are using apache airflow to manage ML pipelines. | not quoted | 70 | quoted | quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | Dataiku | -99 |
339 | Completed (31) | 1,582 | Mathematics | Actuarial Science | -99 | -99 | Coursera IBM Data Science Specialization certificate, Coursera Andrew NG ML courses | Israel Tech Challenge Data Science Fellowship Certification | Israel | 51-250 employees | Data Scientist | -99 | 2 | 1 | 4 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Gaming and Marketing | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | High Relevance | Neutral | Extremely Relevant | Low Relevance | Extremely Relevant | Very Complex | Complex | Complex | Neutral | Complex | Easy | Neutral | 20 | 10 | 20 | 15 | 15 | 5 | 15 | domain knowledge | -99 | -99 | barriers to getting data like cost and logistics | complexity of the data structured/unstructured etc | -99 | time | tools available | -99 | finding the right model that best fits the data | hyperparameter tuning | overfitting | understanding the business problem | identifying metrics for these business problems | -99 | logistics | resources | -99 | identifying the right ways of tracking the models inability to capture new environments | not just identifying that there is a problem but understanding which type of problem it is. there are many possible ones | -99 | -99 | -99 | -99 | domain knowledge | data collection | model evaluation | Frequently | 60 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | MLFlow/DataBricks | not quoted | 90 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Hyperparameter tuning | https://ww2.unipark.de/uc/seml/ |
325 | Completed (31) | 459 | Computer Engineering | computer science | computer engineering | -99 | -99 | -99 | Italy | 11-50 employees | Solution Architect | -99 | 4 | 4 | 5 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Complex | Complex | Complex | Complex | Complex | Complex | Neutral | 15 | 15 | 15 | 15 | 15 | 15 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 50 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | quoted | not quoted | not quoted | quoted | not quoted | -99 | No | -99 | https://l.facebook.com/ |
328 | Completed (31) | 1,568 | Computer Science | Machine Learning | M.Sc. in Computer Science | -99 | -99 | -99 | United States | More than 2,000 employees | Other, which one? | Tech Lead | 7 | 7 | 4 | 3 | 20-50 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Virtual Assistants | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Go | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | CRF | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Very Complex | Complex | Complex | Complex | Complex | Neutral | 10 | 30 | 15 | 20 | 10 | 10 | 5 | -99 | -99 | -99 | Quality of data sources | Privacy | Storage | -99 | -99 | -99 | Hardware | Hyperparameter tuning | -99 | Metrics selection | Clean test sets | -99 | High latency | Model too big to be served online | -99 | Collect feedback from usage | Engagement metrics | -99 | -99 | -99 | -99 | Wrong metrics to measure quality | Bad data quality | Model too big to be served online | Sometimes | 20 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Group of tech leads | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 100 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
337 | Completed (31) | 1,172 | Software Engineering | Data Science and ML | M.Sc. Applied Data Analytics | [Ongoing] Ph.D. in Machine Learning | Udemy x3 | -99 | United Kingdom | 51-250 employees | Data Scientist | -99 | 5 | 3 | 10 | 3 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Research | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | React JS for visualisations and interaction with the project | not quoted | -99 | quoted | Xray scans of security threats, Medical imaging, general purpose models | not quoted | -99 | not quoted | -99 | quoted | Explainability enchancements ; Autoencoder problems for data compression ; Active Learning ; Meta learning ; Neural Architecture Search | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Evolutionary algorithms | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Neutral | High Relevance | Very Complex | Neutral | Complex | Complex | Neutral | Easy | Easy | 25 | 20 | 15 | 15 | 10 | 10 | 5 | Incorrect problem definition | Computational feasibility | Requirement not fit for purpose | Out of distribution samples | Different capturing devices | Data not representative | Normalisation | Selecting salient classes | Augmentations | Learning rate | Picking right architecture | Incorrect loss function | Incorrect metrics | Overfit models | Data leakage or using the test set | Overengineered solutions | Rebuilding after model change | Incompatible frameworks | Too many metrics tracked | No smart tracking | Use of non in-house software can generate data breaches | -99 | -99 | -99 | Incorrect problem definition | Out of distribution samples | Requirement not fit for purpose | Frequently | 100 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 60 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-keras ; NNI ; Custom made ones | -99 |
345 | Completed (31) | 935 | Software Engineering | Web and Data Science, Finance and Accounting | Software Engineering and Management | -99 | -99 | -99 | Austria | 251-500 employees | Data Scientist | -99 | 4 | 1 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Smart Home | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Extremely Relevant | Neutral | Extremely Relevant | Very Complex | Very Complex | Very Complex | Easy | Easy | Complex | Very Complex | 30 | 10 | 30 | 5 | 10 | 5 | 10 | no domain knowledge | making assumptions | -99 | getting good quality data | not enough data | -99 | outliers | bias | dealing with noise | not understanding the algorithm | hyperparameter tuning | -99 | not understanding the metrics correctly and thus making false assupmtions | -99 | -99 | not planning how to integrate the model in the planning phase | -99 | -99 | no experience with this topic and little information is out there | -99 | -99 | -99 | -99 | -99 | data quality | coming up with assumptions that turn out to be false | hyperparameter tuning | Always | 100 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 2 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
346 | Completed (31) | 502 | Software Engineering | Mobile and web applications | Computer Science Information and Security | -99 | -99 | -99 | Turkey | 1-10 employees | Project Lead / Project Manager | -99 | 7 | 2 | 1 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Energy | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Very Complex | Very Complex | Very Complex | Very Complex | Very Complex | 20 | 20 | 20 | 10 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 60 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
347 | Completed (31) | 985 | Mathematics | Computer Science | Statistic and Optimization | -99 | -99 | -99 | Austria | 501-1,000 employees | Data Scientist | -99 | 4 | 5 | 5 | 2 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | c# | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Low Relevance | Neutral | Neutral | Neutral | Very Complex | Complex | Complex | Easy | Neutral | Neutral | Neutral | 40 | 30 | 20 | 3 | 3 | 2 | 2 | bad documentation | multidiscioplinary | long way to formulate clear requirements | cross over several systems | -99 | -99 | poor data quality | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | lack of basic knowledge with customers (mean is most difficult concept) | -99 | -99 | long way to formulate requirements | -99 | -99 | Frequently | 60 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | airflow, gitlab CI/CD | not quoted | 50 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
368 | Completed (31) | 1,269 | -99 | -99 | M.Sc in Computer Science | -99 | -99 | -99 | Switzerland | More than 2,000 employees | Other, which one? | Senior Management | 12 | 0 | 10 | 4 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | financial prediction | quoted | document classification, image classification | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not sure as I am not the developper myself | Extremely Relevant | Neutral | Neutral | High Relevance | High Relevance | Neutral | Extremely Relevant | Easy | Complex | Complex | Very Complex | Easy | Very Complex | Easy | 20 | 10 | 10 | 30 | 10 | 10 | 10 | Solution searching for a problem | Misunderstand of what AI can do and cannot do | -99 | accesses | -99 | -99 | -99 | -99 | -99 | find the balance between accuracy and transparency | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | forgotten. | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | Model Monitoring | -99 | Frequently | 30 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | dataiku | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | dataiku | -99 |
360 | Completed (31) | 24 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ospe.php?SES=f1dc7d7f98d3956eecbd6a3bc14026e6&syid=851780&sid=851781&act=start&preview_mode=1 |
350 | Completed (31) | 1,157 | Computer Science, Mathematics | -99 | Artificial Intelligence | Ph.D. in Computer Science (machine learning) | -99 | -99 | Spain | More than 2,000 employees | Data Scientist | -99 | 14 | 14 | 60 | 20 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Consumer goods industry | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Neutral | Neutral | Neutral | Neutral | Complex | 15 | 20 | 30 | 10 | 10 | 5 | 10 | Different languages business vs DS | Lack of background information | Business trying to propose the solution instead of the problem | Data accesibility accross the company (data silos) | -99 | -99 | Time consuming task | -99 | -99 | Keeping track of the different experiments/Data used/results done | -99 | -99 | -99 | -99 | -99 | lack of proper tools to facilitate the deployment | -99 | -99 | lack of standards for detecting data/model drift | -99 | -99 | -99 | -99 | -99 | Different languages business vs DS | Data accesibility | -99 | Sometimes | 40 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
351 | Completed (31) | 1,724 | Computer Science | -99 | -99 | -99 | -99 | -99 | Austria | 1-10 employees | Developer | -99 | 2 | 1 | 2 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predict next action in a chatbot | quoted | Classification of intents in a chatbot | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Complex | Neutral | Neutral | Complex | Neutral | Neutral | Neutral | 20 | 15 | 10 | 35 | 10 | 5 | 5 | Identifying useful use-cases for ML | -99 | -99 | Finding data sources | -99 | -99 | Bringing data in consistent structure | -99 | -99 | Make optimal use of ML technology available in framework | -99 | -99 | Making right conclusions | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Identifying useful use-cases for ML | Make optimal use of ML technology available in framework | Finding data sources | Sometimes | 40 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
356 | Completed (31) | 260 | Computer Science | -99 | -99 | -99 | -99 | null | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
355 | Completed (31) | 556 | -99 | -99 | -99 | Ph.D. in Computer Science | -99 | -99 | Turkey | 1,001-2,000 employees | Project Lead / Project Manager | -99 | 25 | 5 | 5 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Tailored Waterfall/Agile | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Complex | Complex | Complex | Complex | Neutral | Neutral | 10 | 10 | 20 | 20 | 20 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 35 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | limited docmentataion | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
358 | Completed (31) | 992 | Information Systems | MBA in Business Management | -99 | -99 | Coursera (DeepLearning.ai) | -99 | Brazil | 51-250 employees | Developer | -99 | 18 | 2 | 3 | 2 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | Manufacturing (computer vision for defect detection) | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | 1) Churn detection; 2) Predicting API usage (number of API calls non-linearly related to the expected amount of users) | quoted | Defect and anomaly detection in manufacturing process | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Very Complex | Complex | Neutral | Neutral | Very Easy | Easy | 20 | 30 | 20 | 20 | 5 | 3 | 2 | -99 | -99 | -99 | X | -99 | -99 | -99 | X | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | X | -99 | -99 | -99 | Problem understanding and requirements. We proposed and deployed a custom churn detection model, and in the end it was not considered in the marketing campaign at all. The customer simply asked to not consider it before even seeing the results. | -99 | -99 | Frequently | 50 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 33 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
362 | Completed (31) | 538 | -99 | -99 | Master in Computer Science, MAster in MArketing and Trade Management | -99 | -99 | -99 | Spain | More than 2,000 employees | Project Lead / Project Manager | -99 | 15 | 3 | 2 | 2 | 11-20 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | Very Complex | Neutral | Complex | Complex | 0 | Neutral | Neutral | 10 | 30 | 10 | 20 | 10 | 15 | 5 | x | -99 | -99 | -99 | -99 | -99 | -99 | x | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | x | -99 | -99 | -99 | -99 | -99 | -99 | problem understanding and requirements | data pre-processing | model deployment | Frequently | 40 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://ww2.unipark.de/uc/seml/ |
364 | Completed (31) | 64 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
365 | Completed (31) | 902 | Computer Engineering | machine learning | -99 | -99 | -99 | -99 | Turkey | 51-250 employees | Other, which one? | computer vision engineer | 2 | 2 | 3 | 2 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | object detection, segmentation | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Complex | Complex | Complex | Complex | Complex | Complex | 15 | 30 | 10 | 30 | 5 | 5 | 5 | interpreting the problem vaguely | -99 | -99 | ethical and legal issues | -99 | -99 | normalization and standardization errors | -99 | -99 | experiment and dataset versioning | -99 | -99 | irrelevance between business impact and evaluation metric | -99 | -99 | compilation of model weights to different hardware is cumbersome | -99 | -99 | lack of useful dashboards | -99 | -99 | - | -99 | -99 | data collection | model evaluation | model monitoring | Sometimes | 50 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
366 | Completed (31) | 446 | -99 | -99 | Masters in Industrial Engineering and Management | -99 | -99 | -99 | Portugal | 51-250 employees | Project Lead / Project Manager | -99 | 6 | 6 | 30 | 15 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | quoted | Prescription | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Neutral | High Relevance | Complex | Easy | Neutral | Easy | Easy | Complex | Complex | 20 | 15 | 15 | 5 | 5 | 15 | 25 | Stakeholder buy in | Maintaining business decisions throughout development | -99 | Gathering data from ad-hoc sources | -99 | -99 | Data quality | Data Significance | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Ensuring sustainable infrastructure | Not delaying agile development due to infrastructure setup | -99 | Deploying long term quality assurance | Development team not always available after deployment | -99 | -99 | -99 | -99 | Deploying long term quality assurance | Stakeholder buy in | Maintaining business decisions throughout development | Sometimes | 50 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 70 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-sklearn | -99 |
367 | Completed (31) | 1,248 | Informatics | Software Engineering | Informatics | -99 | -99 | -99 | Germany | 51-250 employees | Other, which one? | Reserach Scientist | 4 | 2 | 4 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Manufacturing | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Anomaly Detection in an Additive Manufacturing Process | quoted | Anomaly Detection in an Additive Manufacturing Process | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | 0 | 0 | Extremely Relevant | Neutral | High Relevance | High Relevance | Extremely Relevant | 0 | Very Complex | Neutral | Complex | Neutral | Neutral | Complex | 15 | 20 | 15 | 15 | 10 | 10 | 15 | What Goal or metrics does the ML model follow? | How can success of the ML model be measured? | -99 | Data Quality | Availability of data | -99 | Missing semantics of data | Missing data | -99 | Defining a proper model architectured | -99 | -99 | Which metrics to select? | -99 | -99 | What is the best suited deployment strategy? | -99 | -99 | What artifacts to mintor? | -99 | -99 | -99 | -99 | -99 | Data availability | Data quality | How to measure success | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
373 | Completed (31) | 849 | Mechanical Engineering | Natural Language Processing | Mechanical Engineering and Computer Engineering | Information Technologies | Udemy, DataCamp, Coursera | other trainings | 0 | More than 2,000 employees | Other, which one? | Research Engineer | 18 | 5 | 3 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Low Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Neutral | Neutral | Neutral | Very Complex | Very Complex | Neutral | Neutral | 10 | 10 | 10 | 35 | 20 | 10 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | 1 | -99 | -99 | -99 | 2 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | 3 | Model Creation and Training | Model Evaluation | Data Collection | Sometimes | 25 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
375 | Completed (31) | 190 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ |
377 | Completed (31) | 766 | -99 | -99 | Computer Science, Business Administration | -99 | -99 | -99 | Austria | More than 2,000 employees | Other, which one? | Technology Consulting Competency Lead | 20 | 0 | 2 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Foreign Currency Account Recognition | quoted | Outlier detection to improve data quality | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | Low Relevance | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | Complex | Neutral | Easy | Complex | Very Complex | Easy | Neutral | 25 | 10 | 10 | 15 | 25 | 5 | 10 | Expectation Mgmt | Education on what ML is | Understanding Business Need | Data Quality | Data availability | GDPR | Data Cleansing | -99 | -99 | Finding suitable patterns | Experiment | Trial and error | representative test data | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Expectation Mgmt | Data Quality | representative test data for evaluation | Sometimes | 20 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | Business | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
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