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Building Powerful and Intelligent Applications with Azure Machine Learning
The document presents a presentation by David Walker on building intelligent applications using Azure Machine Learning, covering key topics such as the definitions of Azure, machine learning, and Azure Machine Learning (azureml). It includes discussions on application integration, API/data management, and practical recommendations for leveraging these technologies to enhance business processes. The content emphasizes the importance of machine learning techniques, examples of applications, and the ease of using Azure tools without extensive prior knowledge.
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Building Powerful and Intelligent Applications with Azure Machine Learning
- 1. Building Powerful and IntelligentApplications w/ Azure Machine Learning David Walker Sitecore 2015 Tech MVP, 2x MS-MVP, Sr Sitecore Architect – Layer One Media
- 2. David Walker • Sitecore2015 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 AREWE HERE? WHY ARE YOU HERE
- 4. WHY ARE WEHERE Building Intelligent Applications
- 5. WHY, WHY, WHY???– KEY TAKE AWAYS
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- 7. ARE YOU CERTIFIED?…. OR CERTIFIABLE?
- 8. Agenda/Goals 1. What isAzure? 2. What is Machine Learning? 3. What is AzureML? 4. DataMarket.Azure 5. Application Integration 6. API/Data Management 7. .NET Core Overview
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- 10. I’m Not aProfessor
- 12. I’m Not aPolitician But if I was
- 14. I’m Not aData Scientist
- 15. I’m Just a“Code Monkey” < I’M DEVELOPER />
- 19.
- 20. TEAM WORK… Accelerate Your Journey ByJoining Mine
- 21. SAVE YOU ITERATIONS…AND HEADACHES
- 25. What Would YouWish For? Your Company? Your You, Your Company, Your Customers Get 3 Wishes
- 26. I’m Here, You’reHere… What’s Your Other Two Wishes?
- 27. You Can BeThe Super Hero!
- 28. At LEAST TheAzure Super Hero!
- 29. At LEAST ASuper Hero To Your Customers & App Users
- 31. The Sky is Blue… andthe birds are singing!
- 32. Why are wehere?
- 33. WHICH WAY DOYOU GO?
- 34.
- 35. If yours was, wouldit be comfortable?
- 36. Would it bea paradise?
- 37. How Far Awayis it? Can You Connect to it?
- 38.
- 39. Bout How Big anIsland are you?
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- 43. Agenda/Goals 1. What isAzure? 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 CloudComputing Platform and Infrastructure Pop Quiz: What is Azure?
- 46. Agenda/Goals 1. What isAzure? 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’sa Pirate’s Favorite Coding Language?
- 48.
- 49. Pop Quiz: What’sa Pirate’s Favorite Letter?
- 50.
- 51. • “Field ofstudy that gives computers the ability to learn without being explicitly programmed”. Arthur Samuel – 1959, source Wikipedia Pop Quiz: What is Machine Learning?
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- 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 CD 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 youwant 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 youwant 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 • Example1 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 isAzure? 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 wasfirst 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.
- 63. • Search DataMarketfor published services/experiments How can you use AzureML today?
- 64. • Set upa 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.
- 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 isAzure? 2. What is Machine Learning? 3. What is AzureML? 4. DataMarket.Azure 5. Application Integration 6. API/Data Management 7. .NET Core Overview
- 69.
- 70. • http://datamarket.azure.com • FindData, ML Experiments and everything else! Azure Marketplace
- 71.
- 72. Agenda/Goals 1. What isAzure? 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 endpoints • http://microsoftazuremachinelearning.azurewebsites.net/Cluste rModel.aspx Application Integration
- 74.
- 75. FACIAL RECOGNITION &IMAGE PROCESSING Microsoft Cognitive Services Facial Recongition?
- 76. FACIAL RECOGNITION &IMAGE PROCESSING Microsoft Cognitive Services
- 77. Battle of theBOTs vs
- 78. Battle of theBOTs vs Beep.. beep Native language Over 6 million!
- 79. Rise of theBOTs
- 80. Agenda/Goals 1. What isAzure? 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 isAzure Data Factory?
- 83.
- 84. Agenda/Goals 1. What isAzure? 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 justa few lines of code!
- 86. Easy Integration for Intelligence Third-Party Data?Piece of Cake!
- 87. Just a fewexamples!
- 88.
- 89. I want to Empowermy Apps/Users
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- 102.
- 103. Accelerate Business Experience Customers – DemandMore, So Deliver More!
- 104. The Evolution of Applications HasBegun It Has Already Begun!
- 105. Got API ? WillIntegrate! And Empower! Any and All!
- 106. # API’s x # DataPoints LIMITLESS OPTIONS
- 107.
- 108.
- 109. The Evolution of AppIntelligence… Now Exponential MIND…BLOWN!
- 110. Don’t Get TooExcited!
- 111. DON’T GET TOOEXCITED!
- 112.
- 113. What if…. No…When… A New Requirement:
- 114. Refactor … yetagain
- 115. Refactor Conditions –Configurable Providers!
- 116.
- 117.
- 118. WHAT’S AT THECORE?
- 119. WHAT’S AT THECORE? ALL the WAY Into the Core!
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- 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
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- 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
Editor's Notes
- #7 Ignorance is bliss?
- #8 Ignorance is bliss?
- #11 The wrong way!
- #29 For some.. They think this is enough…
- #34 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #35 Ignorance is bliss?
- #37 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #38 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #39 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #40 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #41 Ignorance is bliss?
- #42 Ignorance is bliss?
- #43 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #44 Ignorance is bliss?
- #45 Bliss. Ah. Sweet Bliss.. For Customers, Marketing Team and Business
- #46 The wrong way!
- #49 The wrong way!
- #62 The wrong way!
- #71 The wrong way!
- #75 The wrong way!
- #83 The wrong way!
- #87 The wrong way!
- #88 Including Region… in the US = State
- #89 Like everything else in the Sitecore Experience Platform, the Personalization engine and components are very extensible!
- #90 Like everything else in the Sitecore Experience Platform, the Personalization engine and components are very extensible!
- #106 Necessity often drives Innovation
- #107 Necessity often drives Innovation
- #108 Integrate anything! The right way.. From the beginning!
- #109 Integrate anything! The right way.. From the beginning!
- #113 Ignorance is bliss?
- #116 .NET Core! True Cross Platform .NET!
- #117 With simple Provider style organization, you can exponentially Accelerate the Business Experience
- #118 With simple Provider style organization, you can exponentially Accelerate the Business Experience
- #119 With simple Provider style organization, you can exponentially Accelerate the Business Experience
- #120 .NET Core! True Cross Platform .NET!
- #123 iOS, Linux, Xamarin,
- #124 So you don’t have to do it again!
- #125 So you don’t have to do it again!
- #126 So you don’t have to do it again!
- #127 It saves so much time and effort!
- #128 I Interface… ALWAYS INTERFACE!
- #129 The wrong way!
- #130 FileSystem/Storage, etc., etc.
- #131 FileSystem/Storage, etc., etc.
- #132 .NET Core! True Cross Platform .NET!
- #133 .NET Core! True Cross Platform .NET!
- #134 .NET Core! True Cross Platform .NET!
- #135 The wrong way!
- #140 2016 – R and Python – in-database scale .. Quit messing with moving data around. Run it as close to the data as possible Full durable memory-optimized tables, CPU affinity and memory allocation, Resource governance and concurrent execution
- #141 The wrong way!
- #142 The wrong way!