An Introduction to Azure Machine

LearningDouglas M. Kline, Ph.D.

Professor of Information Systems, UNC WilmingtonDatabase by Doug

About Me• From Akron, OH• Professor of Information Systems• Teaching: Database, Software Development, others• Research: Neural Networks, Security, Pedagogy, Analytics, IT Strategy,

etc.• Professional: • DatabaseByDoug: SQL Server Consulting (internals, performance tuning)• DatabaseByDoug: https://www.youtube.com/c/databasebydoug• DatabaseByDoug: http://douglaskline.blogspot.com/

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

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

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

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

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

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

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”

Demo: Public Web Service• Model Setup• Trained Model Block• Data Set Block• Score Block

• Adding Inputs / Outputs• Run• Deploy

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

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

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.

Questions• Questions?

Resources• Azure Portal• portal.azure.com

• Selling a web service in the market• https://github.com/Azure/azure-content-nlnl/blob/master/articles/machine-l

earning/machine-learning-publish-web-service-to-azure-marketplace.md