Spsnyc 2016 machine learning
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This document discusses integrating predictive analytics, SharePoint, and Azure Machine Learning. It begins with an introduction to predictive analytics and machine learning. It then discusses Azure Machine Learning Studio for building machine learning models and creating web services. Finally, it discusses how machine learning models can be consumed in SharePoint Online. The key points are that Azure ML Studio allows easy creation of machine learning models, and these models can then be deployed as web services and consumed in applications like SharePoint.
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Spsnyc 2016 machine learning
- 1. Hottest Buzz Out There: Integrating Predictive Analytics, SharePoint and Azure Machine Learning Fernando Leitzelar, PMP Vice President ITSM
- 2. THANK YOU EVENT SPONSORS We appreciated you supporting the New York SharePoint Community! Diamond, Platinum, Gold, & Silver have tables scattered throughout Please visit them and inquire about their products & services To be eligible for prizes make sure to get your bingo card stamped by ALL sponsors Raffle at the end of the day and you must be present to win!
- 3. CONFERENCE MATERIALS 際際滷s / Demo will be posted on Lanyrd.com http://lanyrd.com/2016/spsnyc Photos posted to our Facebook page https://www.facebook.com/sharepointsaturdaynyc Tweet Us - @SPSNYC or #SPSNYC Sign Up for our NO SPAM mailing list for all conference news & announcements http://goo.gl/7WzmPW Problems / Questions / Complaints / Suggestions Info@SPSNYMetro.com
- 4. Visit ExtaClouds booth for wrist bands! Scallywag's Irish Pub 508 9th Ave, between 38th & 39th. [6 minutes walk] Scallywags also serves food. http://www.scallywagsnyc.com/
- 5. Speaker Fernando Leitzelar, PMP Vice President ITSM Fernando Leitzelar is a senior SharePoint Evangelist and Vice-president with a Large Bank As a consultant he regularly interfaced with clients and development teams to design SharePoint-based solutions. Fernando has progressively held SharePoint positions ranging from developer and administrator to Architect and Manager. He has been a SharePoint Saturday Speaker since 2010, having worked extensively on designing and architecting sophisticated SharePoint based applications. He maintains expertise in Office 365, Azure, SharePoint 2016/2013/2010/2007/2003, BI and Machine Learning Solutions. Twitter: @fleitzelar Blog: http://sharepointusa.wordpress.com
- 6. What and How ? Introduction to ML and Predictive Analytics Predictive Analytics using Machine LearningPredictive Analytics Machine Learning Studio Building ML models Create a Web Service Azure Machine Learning Consume Machine Learning ModelSharePoint Online Agenda
- 7. Predictive Analytics Predicting future performance based on historical data
- 8. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
- 9. ADVANCED ANALYTICS BEYOND BUSINESS INTELLIGENCE
- 10. From Descriptive to Prescriptive Analytics Maturity Level
- 11. What happened ? Reporting: Statistics Why did it happen ? Analysis: Excel, OLAP What is happening ? Monitoring: Dashboards, Scorecards What will happen ? Prediction: Data Mining, Machine Learning Evolution of Predictive Analytics 2000s 1990s 1980s 2010s
- 12. Machine Learning Computer Systems that improve with experience
- 13. CLASSES OF LEARNING PROBLEMS Classification: Assign a category to each item (Chinese | French | Indian | Italian | Japanese restaurant). Regression: Predict a real value for each item (stock/currency value, temperature). Ranking: Order items according to some criterion (web search results relevant to a user query). Clustering: Partition items into homogeneous groups (clustering twitter posts by topic). Dimensionality reduction:Transform an initial representation of items into a lower- dimensional representation while preserving some properties (preprocessing of digital images).
- 14. WHAT IS MACHINE LEARNING? Methods and Systems that Adapt based on recorded data Predict new data based on recorded data Optimize an action given a utility function Extract hidden structure from the data Summarize data into concise descriptions
- 15. MACHINE LEARNING IS NOT Methods and Systems that can yield Garbage-In- Knowledge- Out perform good predictions without data modeling & feature engineering Silver-bullet for all data- driven tasks its a powerful data tool! are a replacement for business rules they augment them!
- 16. TRANSFORMATIONAL
- 17. A Good Machine Learning Tool would allows us to solve extremely hard problems better extract more value from Big Data approach human intelligence drive a shift in business analytics
- 18. Data Science is far too complex today Access to quality ML algorithms, cost is high. Must learn multiple tools to go end2end, from data acquisition, cleaning and prep, machine learning, and experimentation. Ability to put a model into production. This must get simpler, it simply wont scale! PROBLEMS ML NEEDS TO ADDRESS
- 20. PREDICTIVE ANALYTICS AND ML SCENARIOS Predictive maintenance
- 22. ADVANCED ANALYTICS TODAY HARD-TO-REACH SOLUTIONS Break away from industry limitations
- 23. WHAT HAS IT GOT TO DO WITH SHAREPOINT ? ML Studio API M
- 25. AZURE MACHINE LEARNING Azure Portal ML Studio ML API Service Operational Team Data Scientists and Data Professionals Software Developers
- 26. AZURE ML STUDIO
- 27. MICROSOFT AZURE MACHINE LEARNING Built for a cloud-first, mobile-first world
- 28. Step 1 Data Preparation and Feature Engineering Step 2 Train and Evaluate Model Step 3 Deploy Web Service BUILDING ML MODEL
- 30. RECEIVER OPERATING CHARACTERISTIC CURVE
- 31. ROC CURVE
- 32. ROC CURVE
- 33. Reduce complexity to broaden participation MICROSOFT AZURE MACHINE LEARNING FEATURES AND BENEFITS Accessible through a web browser, no software to install; Collaborative work with anyone, anywhere via Azure workspace Visual composition with end2end support for data science workflow; Best in class ML algorithms; Extensible, support for R.
- 34. MICROSOFT AZURE MACHINE LEARNING FEATURES AND BENEFITS Rapid experimentation to create a better model Immutable library of models, search discover and reuse; Rapidly try a range of features, ML algorithms and modeling strategies; Quickly deploy model as Azure web service to our ML API service.
- 35. https://azure.microsoft.com/en-us/documentation/articles/machine-learning- algorithm-choice/ https://azure.microsoft.com/en-us/documentation/services/machine-learning/ Azure Machine Learning Essentials Book https://channel9.msdn.com/blogs/Cloud-and-Enterprise-Premium/Building- Predictive-Maintenance-Solutions-with-Azure-Machine-Learning Channel 9 AZURE MACHINE LEARNING RESOURCES
- 36. KEY CONCEPTS Data Model Parameters Learning Prediction Decision Making Utility Function
- 37. STEPS TO BUILD A MACHINE LEARNING SOLUTION
- 38. AZURE DATA MARKET ML APPLICATIONS http://text-analytics-demo.azurewebsites.net/ https://churn.cloudapp.net http://how-old.net/#