Best practices in building machine learning models in Azure ML
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Microsoft Azure ML Studio provides an easy-to-use interface to build and deploy machine learning models. However, the user must carefully select and configure the modules in order to derive meaningful results. In this presentation, I discuss a case study to highlight best practices in building machine learning models.
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Best practices in building machine learning models in Azure ML
- 1. Microsoft Global AI Bootcamp Best practices in building machine learning models in Azure ML Zeydy Ortiz, Ph. D. zortiz @ datacrunchlab.com www.linkedin.com/in/zortiz @DrZeydy @DataCrunch_Lab
- 2. DataCrunch Lab Data Scientist Computer Engineer Computer Science Performance Engineer About me Zeydy Ortiz #GlobalAIBootcamp @DataCrunch_Lab
- 3. DataCrunch Lab Founded in 2016, ICMM is a nonprofit research-driven agency based in Raleigh, NC Mission: To create a sustainable financial future for consumers CEO: Dr. Diane Chen Research Fellow: Patrick Royal Research Project: Create a Machine Learning system to help credit counseling agencies (CCA) retain consumers enrolled in debt management plans (DMP) #GlobalAIBootcamp @DataCrunch_Lab
- 4. DataCrunch Lab Agenda • AI & ML, what’s the relationship? • About Azure ML • ML Case Study (with examples from Gallery) #GlobalAIBootcamp @DataCrunch_Lab
- 5. DataCrunch Lab AI & ML, what’s the relationship? Source: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ #GlobalAIBootcamp @DataCrunch_Lab
- 6. DataCrunch Lab ML/AI is currently being used in many sectors and business functions Retail Healthcare Financial Industrial Education Pharmaceutical Real Estate Transportation Advertising Manufacturing Legal Utilities Marketing Sales Customer Experience Human Resources #GlobalAIBootcamp @DataCrunch_Lab
- 7. DataCrunch Lab Use cases of ML/AI in business Search Sales lead scoring Demand forecasting Predictive maintenance Fraud detection & prevention Advertisement placement Capacity planning Dynamic pricing Route planning Increased revenue Increased efficiency Reduced cost Increased customer satisfaction #GlobalAIBootcamp @DataCrunch_Lab
- 8. DataCrunch Lab Microsoft Azure Machine Learning Studio studio.azureml.net - Easy drag-and-drop - Extensible - Multiple deployment options #GlobalAIBootcamp @DataCrunch_Lab
- 10. DataCrunch Lab Case Study ML for customer retention in DMP programs #GlobalAIBootcamp @DataCrunch_Lab
- 11. Debt Management Plans (DMP) “A debt management plan sets up a payment schedule for you to repay your debts, with the goal of helping creditors receive the money owed to them and ultimately improving your financial and credit standing.” “It usually takes 3-5 years to complete payments under a debt management program, after which you may be able to reestablish credit.” From National Foundation for Credit Counseling – www.nfcc.org Photo by Francisco T Santos on Unsplash #GlobalAIBootcamp @DataCrunch_Lab
- 12. Why use Machine Learning? Historical data is already been used by credit counseling agencies. However, currently not able to provide personalized service. Photo by chuttersnap on Unsplash #GlobalAIBootcamp @DataCrunch_Lab
- 13. DataCrunch Lab What problem are we solving? Organization’s Challenge Improve customer retention in DMP program ML Problem Clustering Classification Regression Recommender System First step: Identify how long a new consumer is expected to stay in DMP program #GlobalAIBootcamp @DataCrunch_Lab
- 14. What data is available? Demographic Financial Program What is known at enrollment time? #GlobalAIBootcamp @DataCrunch_Lab Photo by Mika Baumeister on Unsplash
- 15. DataCrunch Lab Data is messy Errors in data entry Calculation errors Outliers Many categories Sex: F, W, female, california Age: -1, 104 Debt: $1,765,234 Referral: Yahoo, Web, Organic Consult with subject matter expert to incorporate context and determine what is reasonable #GlobalAIBootcamp @DataCrunch_Lab
- 16. DataCrunch Lab Cleaning Data Checklist Fields not known at enrollment time Missing values Fields with many zeros Fields with near zero variance Highly correlated fields Outliers Categorical fields with many different values Data Leakage Identify and determine how to treat these fields or values - Ignore - Substitute - Remove - Transform - Consolidate #GlobalAIBootcamp @DataCrunch_Lab
- 17. DataCrunch Lab Incorporating best practices in ML 7 12 32 0 5 10 15 20 25 30 35 BEST ALGORITHM PROCESSED DATA RAW DATA Mean Absolute Error #GlobalAIBootcamp @DataCrunch_Lab
- 18. DataCrunch Lab “ ” The No Free Lunch (NFL) theorem states that there is no [machine learning] model that works best for every problem. - Eric Cai Based on work by David H. Wolpert “The Lack of A Priori Distinctions between Learning Algorithms”, 1996 #GlobalAIBootcamp @DataCrunch_Lab
- 19. DataCrunch Lab Machine Learning Modules Azure ML provides many built-in models Can be extended with R & Python Documentation #GlobalAIBootcamp @DataCrunch_Lab
- 20. DataCrunch Lab Experiment in Gallery Predicting Median House Values #GlobalAIBootcamp @DataCrunch_Lab
- 21. DataCrunch Lab Understand the assumptions behind the algorithms Linear regression Predict numeric target House sales price Energy use Taxi fare Poisson regression Predict count data # calls received in a call center # patients arriving in ER # months in program #GlobalAIBootcamp @DataCrunch_Lab
- 22. DataCrunch Lab Assessing performance of algorithms Azure ML Studio provides modules to Split Data Partition and Sample Cross Validate Model Tune Model Hyperparameters #GlobalAIBootcamp @DataCrunch_Lab This is where Azure AutoML can help
- 23. DataCrunch Lab Which model is best for this data set? Use test data set to assess performance #GlobalAIBootcamp @DataCrunch_Lab
- 24. DataCrunch Lab What is the model using to make predictions? Does it make sense? Should we use these fields? #GlobalAIBootcamp @DataCrunch_Lab
- 25. “Start with the end in mind” Deploying the algorithm requires coordination with the organization Options: Web service (API), Batch, Local Photo by Matt Lamers on Unsplash #GlobalAIBootcamp @DataCrunch_Lab
- 26. DataCrunch Lab Key takeaways • Follow industry best practices • The ML problem is not the organization’s problem • Yes, clean your data • Compare multiple algorithms • Be skeptical of your models • Consider your options for deployment #GlobalAIBootcamp @DataCrunch_Lab
- 27. DataCrunch Lab Team capabilities • Data science consulting • Custom software development • Machine Learning, Artificial Intelligence, and Cognitive technologies • Big data & IoT Solutions Innovation Awards Grand Prize Winner Highest Potential Value to Manufacturers #GlobalAIBootcamp @DataCrunch_Lab
- 28. Thank you! Zeydy Ortiz, Ph. D. zortiz @ datacrunchlab.com www.linkedin.com/in/zortiz @DrZeydy @DataCrunch_Lab