An introduction to azure machine learning
- 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. • Professional: • DatabaseByDoug: SQL Server Consulting (internals, performance tuning) • DatabaseByDoug: https://www.youtube.com/c/databasebydoug • DatabaseByDoug: http://douglaskline.blogspot.com/
- 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. 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
- 8. 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
- 9. 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”
- 10. Demo: Public Web Service • Model Setup • Trained Model Block • Data Set Block • Score Block • Adding Inputs / Outputs • Run • Deploy
- 11. Demo: Consume Web Service • Web page test • Excel • Code samples: C#, R, Python, etc. • REST
- 12. What we covered: • What’s Azure? • What’s Azure Machine Learning? • Getting Data • Model Building • Publishing as a Web Service • Consuming the Web Service
- 13. 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. 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