Delivered at Pittsburgh Tech Fest - 6/10/2017 Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate. It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks. Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning. In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data. You will learn to: * Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications * How to Integrate Azure Machine Learning into your existing Applications and Processes * Create Azure Machine Learning Experiments * Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes * Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes * Learn various concepts for abstracting and managing services and api's.

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

12. I’m Not a Politician
But if
I was

14. I’m Not a Data Scientist

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

19. TEAMWORK

20. TEAM WORK…
Accelerate
Your Journey
By Joining Mine

21. SAVE YOU ITERATIONS… AND HEADACHES

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

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

27. You Can Be The Super Hero!

28. At LEAST The Azure Super Hero!

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

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

32. Why are we here?

33. WHICH WAY DO YOU GO?

34. Few Applications
are Islands!

35. If yours was,
would it be
comfortable?

36. Would it be a
paradise?

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

38. Got Rocks?
Lighthouse?

39. Bout How Big
an Island
are you?

40. Are Your
Friends
There?

41. What ?
No Friends?

43. 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

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

46. 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

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

48. RRRRRR…..

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

50. C…..

51. • “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?

52. World Domination?
RISE
OF THE
MACHINES!

53. 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

54. • 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

55. 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

56. 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

57. 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

58. 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

59. 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

60. • 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?

62. Cheat Sheet, anyone?

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

64. • 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?

65. Azure ML
demo

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

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

68. 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

69. MONETIZATION!
SHOW ME THE…

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

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

72. 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

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

74. Application Integration
demo

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

76. FACIAL RECOGNITION & IMAGE PROCESSING
Microsoft Cognitive Services

77. Battle of the BOTs
vs

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

79. Rise of the BOTs

80. 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

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

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

83. API/Data Management
demo

84. 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

85. All in just a few lines of code!

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

87. Just a few examples!

88. As an
Application
Developer,

89. I want to
Empower my
Apps/Users

90. Features
Based
on…

91. Weather

92. Stock
Market
Activity

93. Property
Values

94. People Per
Household

95. Average
Commute
Time?

96. High/Low
Crime Area?

97. Area’s Average
Income

98. Area’s Education
Level

99. Area’s Average
Household Size

100. Area’s %
Water vs Land

101. Nearby
Locations

102. What Use Cases
BenefitYour
Business/Visitors?

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

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

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

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

107. MIND…BLOWN!

108. IMAGINIZATION
Never
Limit
the

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

110. Don’t Get Too Excited!

111. DON’T GET TOO EXCITED!

112. TunnelVision

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

114. Refactor … yet again

115. Refactor Conditions – Configurable
Providers!

116. Keep Them Separated!

117. Into The Core

118. WHAT’S AT THE CORE?

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

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

121. .NET Today

122. .NET Tomorrow

123. Do it Right

124. The First Time

125. IInterface

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

127. Example:
Sitecore.SharedSource.ListRenderer
IDataSource

128. IDataSource

129. But Wait

130. There’s More!

131. AppBlocks.NET

132. 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

133. Questions
&
Ideas?

134. Want More?

135. Get Social

136. Learn Together

137. SQL Server!

138. 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

139. 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