Collab365 Empower-Your-Applications-With-Azure-Machine-Learning
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This document provides an agenda and summary for an online conference about empowering applications with Azure Machine Learning. The agenda includes introductions to Azure, machine learning, Azure ML, the Azure DataMarket, application integration, and API/data management. It also includes a machine learning overview and descriptions of supervised and unsupervised learning. The conference includes a demo of Azure ML and discussions of monetization options and related Azure services like API Management and Data Factory.
Collab365 Empower-Your-Applications-With-Azure-Machine-Learning
- 1. Online Conference June 17th and 18th 2015 WWW.COLLAB365.EVENTS Empower Your Applications with Azure Machine Learning
- 2. WWW.COLLAB365.EVENTS David Walker Tech Aspect Email : dave@radicaldave.com Twitter : @DavidWalker Facebook : LinkedIn : Sitecore Practice Directory, Sitecore MVP Over 20+ years exp, 75% as a Consultant Certified Scrum Master, Scrum Developer MCP in 2003, MCAD & MCSD in 2005 Former Senior App Dev at Microsoft Former two-time Microsoft ASP.NET MVP
- 3. WWW.COLLAB365.EVENTS Agenda • What is Azure? This is Collab365 - You got this! • What is Machine Learning? • What is AzureML? • DataMarket.Azure • Application Integration • API/Data Management
- 4. WWW.COLLAB365.EVENTS POP QUIZ: What is Machine Learning? “Field of study that gives computers the ability to learn without being explicitly programmed”. Arthur Samuel – 1959, source Wikipedia
- 5. WWW.COLLAB365.EVENTS Machine Learning / Predictive Analytics Vision Analytics Recommenda-tion engines Advertising analysis Weather forecasting for business planning Social network analysis Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance Machine learning & predictive analytics are core capabilities that are needed throughout your business
- 6. WWW.COLLAB365.EVENTS Machine Learning Overview • Formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E” - Tom M. Mitchell • Another definition: “The goal of machine learning is to program computers to use example data or past experience to solve a given problem.” – Introduction to Machine Learning, 2nd Edition, MIT Press • ML often involves two primary techniques: – Supervised Learning: Finding the mapping between inputs and outputs using correct values to “train” a model – Unsupervised Learning: Finding patterns in the input data (similar to Density Estimates in Statistics)
- 7. WWW.COLLAB365.EVENTS Machine Learning Data: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Rules, or Algorithms: about, Learning, language – Spelling and sounding builds words Learning about language. – Words build sentences Learning, or Abstraction: Any new understanding proceeds from previous knowledge.
- 8. WWW.COLLAB365.EVENTS Supervised Learning1. Used when you want to predict unknown answers from answers you already have – requires data which shows the answers you can get now 2. Data is divided into two parts: the data you will use to “teach” the system (data set), and the data you will use to see if the computer’s algorithms are accurate (test set) 3. After you select and clean the data, you select data points that show the right relationships in the data. The answers are “labels”, the categories/columns/attributes are “features” and the values are…values. 4. Then you select an algorithm to compute the outcome. (Often you choose more than one) 5. You run the program on the data set, and check to see if you got the right answer from the test set. 6. Once you perform the experiment, you select the best model. This is the final output – the model is then used against more data to get the answers you need
- 9. WWW.COLLAB365.EVENTS Unsupervised Learning1. Used when you want to find unknown answers – mostly groupings - directly from data 2. No simple way to evaluate accuracy of what you learn 3. Evaluates more vectors, groups into sets or classifications 4. Start with the data 5. Apply algorithm 6. Evaluate groups
- 10. WWW.COLLAB365.EVENTS Unsupervised Learning • Example 1 example A Example 2 example B Example 3 example C example A example B example C Example 1 Example 2 Example 3
- 11. WWW.COLLAB365.EVENTS Agenda • What is Azure? This is Collab365 - You got this! • What is Machine Learning? • What is AzureML? • Market Place • Application Integration • API/Data Management
- 13. WWW.COLLAB365.EVENTS Why is AzureML so Awesome? • Google was first with just a simple Prediction Service, but it required a lot of thought/work in building appropriate data sets • AzureML is less restrictive on data sets and with a much friendlier set of tools has made it so that anyone can do it – no PhD required. • Then, easily integrate it into your applications, processes – even Excel.
- 14. WWW.COLLAB365.EVENTS How can you use AzureML today? • Search DataMarket.Azure.com for published services/experiments • Text Analytics – Sentiment • Twitter Sentiment Analysis • Lexicon Based Sentiment Analysis
- 15. WWW.COLLAB365.EVENTS Create your own AzureML experiments? • Set up a Microsoft Azure Account • Set up a Storage Account • Load Data • Set up an AzureML Workspace • Accessing AzureML Studio • AzureML Studio Tour
- 17. WWW.COLLAB365.EVENTS 0 – The bar was closed before they determined the most efficient door to enter. 10 Data Scientist standing outside a bar, how many enter?
- 18. WWW.COLLAB365.EVENTS Agenda • What is Azure? This is Collab365 - You got this! • What is Machine Learning? • What is AzureML? • DataMarket.Azure • Application Integration • API/Data Management
- 20. WWW.COLLAB365.EVENTS Agenda • What is Azure? This is Collab365 - You got this! • What is Machine Learning? • What is AzureML? • DataMarket.Azure • Application Integration • API/Data Management
- 21. WWW.COLLAB365.EVENTS Application Integration • Calling AzureML end points • http://microsoftazuremachinelearning.azurewebsites.net/Cl usterModel.aspx
- 22. WWW.COLLAB365.EVENTS Agenda • What is Azure? This is Collab365 - You got this! • What is Machine Learning? • What is AzureML? • DataMarket.Azure • Application Integration • API/Data Management
- 24. WWW.COLLAB365.EVENTS What is Azure API Management? • Service Catalog • Monitoring • Abstraction
- 25. WWW.COLLAB365.EVENTS What is Azure API Management? • http://azure.microsoft.com/en- us/documentation/articles/api-management-get-started/
- 27. WWW.COLLAB365.EVENTS What is Azure Data Factory? • http://azure.microsoft.com/en-us/services/data-factory/
- 28. WWW.COLLAB365.EVENTS Review • Azure • Machine Learning • AzureML • DataMarket.Azure • Application Integration • API/Data Management • Questions?
- 29. WWW.COLLAB365.EVENTS Resources • http://MicrosoftVirtualAcademy.com • http://channel9.msdn.com • http://blogs.technet.com/b/machinelearning/ • DataMarket.Azure.com • Text Analytics – Sentiment • Twitter Sentiment Analysis • Lexicon Based Sentiment Analysis • Connect with me! Twitter: @DavidWalker, LinkedIn, Facebook, http://RadicalDave.com
- 30. WWW.COLLAB365.EVENTS Stay tuned for more great sessions …