Denver Dev Day - Smart Apps with Azure ML
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This document discusses machine learning and Azure Machine Learning. It defines machine learning and provides quotes about machine learning from experts. It then lists examples of machine learning applications and business uses. The document outlines skills needed for applied machine learning and describes the Azure Machine Learning process and tools. It discusses common machine learning challenges and provides resources for the Azure Machine Learning ecosystem.
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Denver Dev Day - Smart Apps with Azure ML
- 1. Sponsors
- 2. Smart Apps with Azure ML CHRIS MCHENRY VP OF TECHNOLOGY, INTEGRO HTTP://CMCHENRY.COM @CAMCHENRY
- 3. “Machine learning is a way of getting computers to know things when they see them by producing for themselves the rules their programmers cannot specify. The machines do this with heavy-duty statistical analysis of lots and lots of data.” “Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel (1959) “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” Tom Mitchell (1998) “A breakthrough in Machine Learning would be worth ten Microsoft’s” Bill Gates
- 5. ML Examples FROM THE PRESS Spam Filtering Google/Bing Ad Targeting Postal Service Mail Sorting Cortana Amazon/Netflix Recommendations Credit Card Fraud Detection Deep Blue/Watson How-Old.net BUSINESS APPS SMART APPS Automated Workflow Routing Automated Filing User Suggestions Customers Likely to Buy Customers Likely to Leave Product Pricing Order Anomalies
- 6. Applied ML – Skills Needed BYOD ◦ Bring Your Own Development skills ◦ REST Data Processing/Cleansing ◦ SQL/NoSQL ◦ R and/or Python ◦ Hadoop/HD Insight/Azure Stream Analytics The Right Attitude ◦ Persistence and confidence to understand a complex subject ◦ Unbridled curiosity to explore and iterate and possibly fail ◦ Creativity to find alternatives when you are blocked
- 7. Process
- 8. ML Studio Workspace Experiment - Modules ◦ Training ◦ Scoring DataSet ◦ Direct Upload – 10GB Limit ◦ Reader – Azure Blob, Web Page, Odata, SQL Azure, Hive, etc ◦ R or Python Module Web Services
- 9. Regression
- 10. Classification
- 11. Clustering
- 13. Demo 1. Create a Training Experiment – Select a Model 2. Create a Scoring Experiment – Prep Selected Model for Runtime 3. Publish as a Web Service – Operationalize a Web Service 4. Consume a Web Service – Get Predictions from your App
- 14. Common ML Challenges UNDERFITTING - BIAS OVERFITTING - VARIANCE 1. Add more features 2. Generate features 3. Evaluate training data 1. Reduce features – dimensionality reduction 2. Add more training data 3. Evaluate training data
- 15. Ecosystem Site/ML Studio/Docs: http://azure.microsoft.com/en-us/services/machine-learning/ Gallery: http://gallery.azureml.net/ Azure Marketplace: http://datamarket.azure.com/browse/data?category=machine-learning Blog: http://blogs.technet.com/b/machinelearning/ Forum: https://social.msdn.microsoft.com/Forums/azure/en-US/home?forum=MachineLearning Stack Overflow: http://stackoverflow.com/questions/tagged/azure-ml Webinars: https://azureinfo.microsoft.com/BigDataAdvancedAnalyticsWebinars.html
- 16. Books Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes– Barga, Tok, and Fontama, Apress, 2014 Azure Machine Learning – Jeff Barnes, Microsoft Press, 2015 Data Science in the Cloud with Microsoft Azure Machine Learning and R – Stephen Elston, O’Reilly, 2015
Editor's Notes
- ML Algorithms can combine more data in an analysis than any human possibly could.
- Why Cloud Computing Growth of Data and Connected Devices Example Use Cases - People are using it and making money Services Like Azure ML are democratizing Machine Learning – You don’t have to be Microsoft, Google or Amazon to use this technology.