Building Powerful and Intelligent Applications with Azure Machine Learning

1. Music City Code Building Powerful and Intelligent Applications with Azure Machine Learning David Walker Sitecore 2015 Tech MVP, 2x MS-MVP, Sr Sitecore Architect – Layer One Media

2. David Walker • Sitecore 2015 Technology MVP • Former two-time Microsoft ASP.NET MVP • Senior Sitecore Architect – Layer One Media • Sitecore Certified Developer I & II – 5.3 • Over 25+ years exp, 75% as a Consultant • Certified Scrum Master, Scrum Developer • MCP in 2003, MCAD & MCSD in 2005 • Former Senior App Dev Manager at Microsoft • TechFests.com founder – 12th year of TulsaTechFest.com • SITECOREDAVE.com, RADICALDAVE.com, “Mr. TechFest” ConnectwithMe Email:dave@RadicalDave.com Twitter:@DavidWalker Blog:RadicalDave.com

3. Music City Code

4. • WHY ARE WE HERE? WHY ARE YOU HERE

5. WHY ARE WE HERE Building Intelligent Sitecore Applications

6. WHY, WHY, WHY??? – KEY TAKE AWAYS

7. DEMO

8. ARE YOU CERTIFIED? …. OR CERTIFIABLE?

9. Agenda/Goals 1. What is Azure? 2. What is Machine Learning? 3. What is AzureML? 4. DataMarket.Azure 5. Application Integration 6. API/Data Management 7. .NET Core Overview

10. How Many Cups?

11. I’m Not a Professor

12. I’m Not a Politician But if I was

13. TEAMWORK

14. TEAM WORK… Accelerate Your Journey By Joining Mine

15. SAVE YOU ITERATIONS… AND HEADACHES

16. What Would You Wish For? Your Company? Your You, Your Company, Your Customers Get 3 Wishes

17. I’m Here, You’re Here… What’s Your Other Two Wishes?

18. You Can Be The Super Hero!

19. At LEAST The Azure Super Hero!

20. At LEAST A Super Hero To Your Customers & App Users

21. The Sky is Blue… and the birds are singing!

22. Why are we here?

23. WHICH WAY DO YOU GO?

24. Few Applications are Islands!

25. If yours was, would it be comfortable?

26. Would it be a paradise?

27. How Far Away is it? Can You Connect to it?

28. Got Rocks? Lighthouse?

29. Bout How Big an Island are you?

30. Are Your Friends There?

31. What ? No Friends?

33. • Microsoft’s Cloud Computing Platform and Infrastructure Pop Quiz: What is Azure?

35. • “Field of study that gives computers the ability to learn without being explicitly programmed”. Arthur Samuel – 1959, source Wikipedia Pop Quiz: What is Machine Learning?

36. World Domination? RISE OF THE MACHINES!

37. Pop Quiz: What’s a Pirate’s Favorite Coding Language?

38. RRRRRR…..

39. Pop Quiz: What’s a Pirate’s Favorite Letter?

40. C…..

41. 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

42. • 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) Machine Learning Overview

43. 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. Machine Learning

44. 1.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 Supervised Learning

45. 1.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 Unsupervised Learning

46. 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

47. 0 – The bar was closed before they determined the most efficient door to enter. 10 Data Scientist standing outside a bar, how many enter?

49. • 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. Why is AzureML so Awesome?

50. • Search DataMarket for published services/experiments How can you use AzureML today?

51. • Set up a Microsoft Azure Account • Set up a Storage Account • Load Data • Set up an AzureML Workspace • Accessing AzureML Studio • AzureML Studio Tour Create your own AzureML experiments?

52. Azure ML demo

54. MONETIZATION! SHOW ME THE…

55. • http://datamarket.azure.com • Find Data, ML Experiments and everything else! Azure Marketplace

56. Azure Marketplace Cortana Intelligence Gallery gallery.cortanaintelligence.com demo

58. •Calling AzureML end points • http://microsoftazuremachinelearning.azurewebsites.net/Cluste rModel.aspx Application Integration

59. Application Integration demo

60. FACIAL RECOGNITION & IMAGE PROCESSING Microsoft Cognitive Services

61. FACIAL RECOGNITION & IMAGE PROCESSING Microsoft Cognitive Services Facial Recongition?

63. • Service Catalog • Monitoring • Abstraction http://azure.microsoft.com/en-us/documentation/articles/api- management-get-started/ What is Azure API Management?

64. • http://azure.microsoft.com/en-us/services/data-factory/ What is Azure Data Factory?

65. API/Data Management demo

67. .NET 3.0 Pop Quiz: What did Microsoft release in beta in 2006?

68. 1. WCF 2. WPF 3. WF 4. CardSpace Pop Quiz: What were the four components of .NET 3.0?

69. Accelerate Your Journey By Joining Mine

70. ALL the WAY Back to the Core!

71. All in just a few lines of code!

72. Don’t Get Too Excited!

73. DON’T GET TOO EXCITED!

74. TunnelVision

75. Easy Integration for Intelligence Third-Party Data? Piece of Cake!

76. Just a few examples!

77. As an Application Developer,

78. I want to Empower my Apps/Users

79. Features Based on…

80. Weather

81. Stock Market Activity

82. Property Values

83. People Per Household

84. Average Commute Time?

85. High/Low Crime Area?

86. Area’s Average Income

87. Area’s Education Level

88. Area’s Average Household Size

89. Area’s % Water vs Land

90. Nearby Locations

91. What Use Cases BenefitYour Business/Visitors?

92. Accelerate Business Experience Customers – Demand More, So Deliver More!

93. IMAGINIZATION Never Limit the

94. The Evolution of Applications Has Begun It Has Already Begun!

95. Got API ? Will Integrate! And Empower! Any and All!

96. # API’s x # Data Points LIMITLESS OPTIONS

97. MIND…BLOWN!

98. IMAGINIZATION Never Limit the

99. The Evolution of App Intelligence… Now Exponential MIND…BLOWN!

100. But Wait

101. There’s More!

102. What if…. No… When… A New Requirement:

103. Refactor … yet again

104. Refactor Conditions – Configurable Providers!

105. Keep Them Separated!

106. Into The Core

107. WHAT’S AT THE CORE?

108. WHAT’S AT THE CORE? ALL the WAY Into the Core!

109. Cross-platform Open source Flexible Modular .NET Core

110. .NET Today

111. .NET Tomorrow

112. Do it Right

113. The First Time

114. IInterface

115. Example: Sitecore.SharedSource.ListRenderer GetSitecoreContent GetWebContent GetDbContent

116. Example: Sitecore.SharedSource.ListRenderer IDataSource

117. IDataSource

118. Agenda/Goals - REVIEW 1. What is Azure? 2. What is Machine Learning? 3. What is AzureML? 4. DataMarket.Azure 5. Application Integration 6. API/Data Management 7. .NET Core Overview

119. Questions & Ideas?

120. Want More?

121. Get Social

122. Learn Together

123. SQL Server!

124. Resources http://MicrosoftVirtualAcademy.com http://BuildAzure.com @BuildAzure @MVPAward SQLPASS.org – WebCast – Feb 11th – Enabling Advanced Full Text Search of SQL Server Data using Azure Search SQLPASS.org – WebCast – Feb 25th on DocumentDB @ryancrawcour – Program Manager – DocumentDB http://blogs.msdn.com/b/documentdb/ @liamca – Program Manager – Azure Search http://GitHub.com/SitecoreDave/ Connect with me! Twitter: @DavidWalker, LinkedIn, Facebook, http://RadicalDave.com

125. Music City Code

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