This document provides an introduction to Azure Machine Learning. It discusses what Azure and Azure Machine Learning are, and walks through the process of getting data, building models, publishing models as web services, and consuming web services. It demonstrates these steps in Azure Machine Learning Studio using a dataset on credit card default. The document concludes by discussing pricing and monetization options for Azure Machine Learning web services.

1. An Introduction to Azure
Machine Learning
Douglas M. Kline, Ph.D.
Professor of Information Systems, UNC Wilmington
Database by Doug

2. About Me
• From Akron, OH
• Professor of Information Systems
• Teaching: Database, Software Development, others
• Research: Neural Networks, Security, Pedagogy, Analytics, IT Strategy,
etc. (Google Scholar profile)
• Professional:
• DatabaseByDoug: SQL Server Consulting (internals, performance tuning)
• DatabaseByDoug: https://www.youtube.com/c/databasebydoug
• DatabaseByDoug: http://douglaskline.blogspot.com/
• LinkedIn: https://www.linkedin.com/in/douglaskline/

3. Overview
• What’s Azure?
• What’s Azure Machine Learning?
• Getting Data
• Model Building
• Publishing as a Web Service
• Consuming the Web Service
• Conclusion

4. What’s Azure?
• Microsoft’s cloud computing services platform
• Storage, Bandwidth, Computing, services
• Self-serve
• Metered – pay for what you use
• Helps to be aware of charges

5. What’s Azure Machine Learning?
• Cloud service for analytics
• Machine Learning Studio
• Visual experiment designer, drag and drop
• Pre-defined method blocks
• Classification, clustering, time series, prediction, statistics, etc.
• Data input, output, transformations, etc.
• Experiment control: data partitioning, model definition, training, scoring, evaluation, etc.
• R blocks
• Deploy models as Web Services
• web service marketplace

6. Getting Data
• Sources: SQL, Storage, CSV
• Manipulation: SQL, column selection, sampling
• Basic Stats
• R block
• Cache Data Set
• Save Data Set

7. Our Data Set – Taiwan CC Default
• https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
• October – default on credit card – 1/0 (predict this!)
• Credit line amount
• Apr – September
• Bill amount
• Amount paid
• Delay in payment, number of months
• Demographics
• Gender
• Education
• Marital Status

8. Demo: Get Data from Azure SQL
• Input Block
• Wizard
• SELECT a sample – randomUniform
• Visualizations
• Summarize Block
• Feature Selection
• Automatic
• Interactive
• Save as Data Set
• Simple R Block

9. Demo: Model Building
• Import Data Set
• Split Data: training / testing
• Two 2-Group Classification Models:
• NN
• Boosted Decision Tree
• Model training
• Model scoring (training/testing)
• Model evaluation
• Training vs. testing
• Model A vs. Model B
• Recalibrate
• Save Trained Model

10. Metrics
• Accuracy – % correctly classified, positive or negative
• Precision - % of positives correctly classified
• Recall - % positive predictions correct
• F1 – evenly weighted Precision and Recall
• ROC – Left side is Threshold=1, Right side is Threshold=0
• Recall Curve
• AUC – area under curve across all thresholds, max = 1
• Precision/Recall – as threshold changes
• Lift chart – “costed”

11. Demo: Public Web Service
• Model Setup
• Trained Model Block
• Data Set Block
• Score Block
• Adding Inputs / Outputs
• Run
• Deploy

12. Demo: Consume Web Service
• Web page test
• Excel
• Code samples: C#, R, Python, etc.
• REST

13. What we covered:
• What’s Azure?
• What’s Azure Machine Learning?
• Getting Data
• Model Building
• Publishing as a Web Service
• Consuming the Web Service

14. Conclusion
• MS has thought through integration of analytics into systems
• Input
• Output
• New blocks added all the time
• Re-calibration, re-deploy, versioning, etc. possible / automate-able
• Powershell
• Metered/charged: storage, compute, database transaction units,
bandwidth
• Sell-able as a web service
• Must be approved as a seller, have a pricing plan, approved as a service, etc.

15. Questions?
• Thanks!

16. Resources
• Azure Portal
• portal.azure.com
• Selling a web service in the market
• https://github.com/Azure/azure-content-nlnl/blob/master/articles/machine-
learning/machine-learning-publish-web-service-to-azure-marketplace.md