Building Powerful and Intelligent Applications with Azure Machine Learning

1. Building Powerful and Intelligent Applications w/ 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. • WHY ARE WE HERE? WHY ARE YOU HERE

4. WHY ARE WE HERE Building Intelligent Applications

5. WHY, WHY, WHY??? – KEY TAKE AWAYS

6. DEMO

7. ARE YOU CERTIFIED? …. OR CERTIFIABLE?

8. 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

9. How Many Cups?

10. I’m Not a Professor

11. I’m Not a Politician But if I was

12. I’m Not a Data Scientist

13. I’m Just a “Code Monkey” < I’M DEVELOPER />

14. TEAMWORK

15. TEAM WORK… Accelerate Your Journey By Joining Mine

16. SAVE YOU ITERATIONS… AND HEADACHES

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

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

19. You Can Be The Super Hero!

20. At LEAST The Azure Super Hero!

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

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

23. Why are we here?

24. WHICH WAY DO YOU GO?

25. Few Applications are Islands!

26. If yours was, would it be comfortable?

27. Would it be a paradise?

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

29. Got Rocks? Lighthouse?

30. Bout How Big an Island are you?

31. Are Your Friends There?

32. What ? No Friends?

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

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

37. RRRRRR…..

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

39. C…..

40. • “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?

41. World Domination? RISE OF THE MACHINES!

42. 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

43. • 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

44. 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

45. 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

46. 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

47. 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

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. Cheat Sheet, anyone?

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

52. • 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?

53. Azure ML demo

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

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

57. MONETIZATION! SHOW ME THE…

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

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

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

62. Application Integration demo

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

64. FACIAL RECOGNITION & IMAGE PROCESSING Microsoft Cognitive Services

65. Battle of the BOTs vs

66. Battle of the BOTs vs Beep.. beep Native language Over 6 million!

67. Rise of the BOTs

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

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

71. API/Data Management demo

73. All in just a few lines of code!

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

75. Just a few examples!

76. As an Application Developer,

77. I want to Empower my Apps/Users

78. Features Based on…

79. Weather

80. Stock Market Activity

81. Property Values

82. People Per Household

83. Average Commute Time?

84. High/Low Crime Area?

85. Area’s Average Income

86. Area’s Education Level

87. Area’s Average Household Size

88. Area’s % Water vs Land

89. Nearby Locations

90. What Use Cases BenefitYour Business/Visitors?

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

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

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

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

95. MIND…BLOWN!

96. IMAGINIZATION Never Limit the

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

98. Don’t Get Too Excited!

99. DON’T GET TOO EXCITED!

100. TunnelVision

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

102. Refactor … yet again

103. Refactor Conditions – Configurable Providers!

104. Keep Them Separated!

105. Into The Core

106. WHAT’S AT THE CORE?

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

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

109. .NET Today

110. .NET Tomorrow

111. Do it Right

112. The First Time

113. IInterface

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

115. Example: Sitecore.SharedSource.ListRenderer IDataSource

116. IDataSource

117. But Wait

118. There’s More!

119. AppBlocks.NET

120. 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

121. Questions & Ideas?

122. Want More?

123. Get Social

124. Learn Together

125. SQL Server!

126. 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

127. Resources http://MicrosoftVirtualAcademy.com http://BuildAzure.com @BuildAzure @MVPAward Topic: Graph data processing with SQL Server 2017 and Azure SQL DB Speakers: Shreya Verma & Arvind Shyamsundar Date & Time: Thu, Jun 8 2017 19:00 UTC (Check local time here) Registration: https://attendee.gotowebinar.com/register/13127249068 75249409 http://GitHub.com/SitecoreDave/ Connect with me! Twitter: @DavidWalker, LinkedIn, Facebook, http://RadicalDave.com

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