Delivered @ MusicCityCode 6/2/2017
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.
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
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
42. 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
43. • Microsoft’s Cloud Computing Platform and Infrastructure
Pop Quiz: What is Azure?
44. 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
45. • “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?
51. 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
52. • 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
53. 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
54. 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
55. 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
56. 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
57. 0 – The bar was closed before
they determined the most
efficient door to enter.
10 Data Scientist standing
outside a bar, how many enter?
58. 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
59. • 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?
60.
61. • Search DataMarket for published services/experiments
How can you use
AzureML today?
62. • 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?
64. 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
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. •Calling AzureML end points
• http://microsoftazuremachinelearning.azurewebsites.net/Cluste
rModel.aspx
Application Integration
73. 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
74. • Service Catalog
• Monitoring
• Abstraction
http://azure.microsoft.com/en-us/documentation/articles/api-
management-get-started/
What is Azure API Management?
77. 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
129. 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
135. 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
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Ignorance is bliss?
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Ignorance is bliss?
Ignorance is bliss?
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
Ignorance is bliss?
Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
The wrong way!
The wrong way!
The wrong way!
The wrong way!
The wrong way!
The wrong way!
The wrong way!
Join my on My Journey… and learn from my experience
Including Region… in the US = State
Ignorance is bliss?
Like everything else in the Sitecore Experience Platform, the Personalization engine and components are very extensible!
Like everything else in the Sitecore Experience Platform, the Personalization engine and components are very extensible!
Necessity often drives Innovation
Necessity often drives Innovation
Integrate anything! The right way.. From the beginning!
Integrate anything! The right way.. From the beginning!
.NET Core! True Cross Platform .NET!
.NET Core! True Cross Platform .NET!
.NET Core! True Cross Platform .NET!
With simple Provider style organization, you can exponentially Accelerate the Business Experience
With simple Provider style organization, you can exponentially Accelerate the Business Experience
With simple Provider style organization, you can exponentially Accelerate the Business Experience
.NET Core! True Cross Platform .NET!
iOS, Linux, Xamarin,
So you don’t have to do it again!
So you don’t have to do it again!
So you don’t have to do it again!
It saves so much time and effort!
I Interface… ALWAYS INTERFACE!
The wrong way!
FileSystem/Storage, etc., etc.
FileSystem/Storage, etc., etc.
The wrong way!
2016 – R and Python – in-database scale .. Quit messing with moving data around. Run it as close to the data as possibleFull durable memory-optimized tables, CPU affinity and memory allocation, Resource governance and concurrent execution