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ForewordI’m thrilled to be able to share these Microsoft Azure Essentials ebooks with you. The power thatMicrosoft Azure gives you is thrilling but not unheard of from Microsoft. Many don’t realize thatMicrosoft has been building and managing datacenters for over 25 years. Today, the company’s clouddatacenters provide the core infrastructure and foundational technologies for its 200-plus onlineservices, including Bing, MSN, Office 365, Xbox Live, Skype, OneDrive, and, of course, Microsoft Azure.The infrastructure is comprised of many hundreds of thousands of servers, content distributionnetworks, edge computing nodes, and fiber optic networks. Azure is built and managed by a team ofexperts working 24x7x365 to support services for millions of customers’ businesses and living andworking all over the globe.Today, Azure is available in 141 countries, including China, and supports 10 languages and 19currencies, all backed by Microsoft's $15 billion investment in global datacenter infrastructure. Azure iscontinuously investing in the latest infrastructure technologies, with a focus on high reliability,operational excellence, cost-effectiveness, environmental sustainability, and a trustworthy onlineexperience for customers and partners worldwide.Microsoft Azure brings so many services to your fingertips in a reliable, secure, and environmentallysustainable way. You can do immense things with Azure, such as create a single VM with 32TB ofstorage driving more than 50,000 IOPS or utilize hundreds of thousands of CPU cores to solve yourmost difficult computational problems.Perhaps you need to turn workloads on and off, or perhaps your company is growing fast! Somecompanies have workloads with unpredictable bursting, while others know when they are about toreceive an influx of traffic. You pay only for what you use, and Azure is designed to work with commoncloud computing patterns.From Windows to Linux, SQL to NoSQL, Traffic Management to Virtual Networks, Cloud Services toWeb Sites and beyond, we have so much to share with you in the coming months and years.I hope you enjoy this Microsoft Azure Essentials series from Microsoft Press. The first three ebookscover fundamentals of Azure, Azure Automation, and Azure Machine Learning. And I hope you enjoyliving and working with Microsoft Azure as much as we do.6
IntroductionMicrosoft Azure Machine Learning (ML) is a service that a developer can use to build predictiveanalytics models (using training datasets from a variety of data sources) and then easily deploy thosemodels for consumption as cloud web services. Azure ML Studio provides rich functionality to supportmany end-to-end workflow scenarios for constructing predictive models, from easy access to commondata sources, rich data exploration and visualization tools, application of popular ML algorithms, andpowerful model evaluation, experimentation, and web publication tooling.This ebook will present an overview of modern data science theory and principles, the associatedworkflow, and then cover some of the more common machine learning algorithms in use today. We willbuild a variety of predictive analytics models using real world data, evaluate several different machinelearning algorithms and modeling strategies, and then deploy the finished models as machine learningweb service on Azure within a matter of minutes. The book will also expand on a working AzureMachine Learning predictive model example to explore the types of client and server applications youcan create to consume Azure Machine Learning web services.The scenarios and end-to-end examples in this book are intended to provide sufficient informationfor you to quickly begin leveraging the capabilities of Azure ML Studio and then easily extend thesample scenarios to create your own powerful predictive analytic experiments. The book wraps up byproviding details on how to apply “continuous learning” techniques to programmatically “retrain” AzureML predictive models without any human intervention.Who should read this bookThis book focuses on providing essential information about the theory and application of data scienceprinciples and techniques and their applications within the context of Azure Machine Learning Studio.The book is targeted towards both data science hobbyists and veterans, along with developers and ITprofessionals who are new to machine learning and cloud computing. Azure ML makes it just asapproachable for a novice as a seasoned data scientist, helping you quickly be productive and on yourway towards creating and testing machine learning solutions.Detailed, step-by-step examples and demonstrations are included to help the reader understandhow to get started with each of the key predictive analytic algorithms in use today and theircorresponding implementations in Azure ML Studio. This material is useful not only for those who haveno prior experience with Azure Machine Learning, but also for those who are experienced in the field ofdata science. In all cases, the end-to-end demos help reinforce the machine learning concepts withconcrete examples and real-life scenarios. The chapters do build on each other to some extent;however, there is no requirement that you perform the hands-on demonstrations from previous7
Page 1 and 2: Azure MachineLearningMicrosoft Azur
Page 3 and 4: Hear aboutit first.Get the latest n
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Page 7 and 8: Supervised learning ...............
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Page 31 and 32: Data It’s all about the data. Her
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FIGURE 3-16 Visualization of the Ad
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The workclass field is designated a
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Figure 3-20 displays a screenshot o
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FIGURE 3-22 Opening the column sele
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FIGURE 3-24 Training our new Azure
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FIGURE 3-26 Visualizing the results
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Evaluate the modelOne of the most c
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Receiver Operator Characteristic (R
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FIGURE 3-31 After the Prepare Web S
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If we click the toggle button again
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FIGURE 3-37 The Azure Machine Learn
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provide access to data over the Web
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Note that the sample Request Body d
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FIGURE 3-47 A sample Response Body
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Sample Code Here’s the best part:
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Testing the Azure Machine Learning
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FIGURE 3-53 Processing the Azure Ma
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ce-to-azure-marketplace/http://data
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The following is a listing of the a
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FIGURE 4-2 Select the specific impl
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FIGURE 4-5 Sample code for invoking
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FIGURE 4-7 Invoking the NuGet Packa
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FIGURE 4-9 Sample C# code to invoke
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Now watch what happens if we replac
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To do this, simply click the menu o
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FIGURE 4-16 Invoking the Azure Mach
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They fall short, however, in terms
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FIGURE 4-19 Selecting an Empty ASP.
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FIGURE 4-21 Selecting a Web Form it
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Validating the user inputTo further
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FIGURE 4-26 Invoking the Azure Mach
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Create a web service using ASP.NET
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FIGURE 4-29 Selecting the Visual St
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FIGURE 4-31 Adding a new item to th
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FIGURE 4-33 C# class data structure
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FIGURE 4-35 Adding the empty scaffo
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FIGURE 4-38 Installing the NuGet pa
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FIGURE 4-41 The IncomePredictionCon
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{Inputs = new Dictionary<string, St
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}incomePredResult.Sex = mlResultsAr
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FIGURE 4-44 A jQuery Mobile web app
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var iSex = document.getElementById(
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Chapter 5Regression analyticsIn the
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claims. It is easily seen how these
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Note that in this case, the Azure M
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FIGURE 5-4 The Upload A New Dataset
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FIGURE 5-7 Azure ML Studio: Selecti
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FIGURE 5-9 Selecting the option to
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FIGURE 5-12 The Clean Missing Data
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to .80. In this context, .80 means
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FIGURE 5-16 The automobile price pr
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FIGURE 5-18 The option to Visualize
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After clicking Publish Web Service
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FIGURE 5-23 The Azure ML Studio Reg
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SummaryIn this chapter, we took a d
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unsupervised machine learning, the
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Cluster analysis is not necessarily
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Milk Annual spending on milk produc
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Now that we have the Reader module
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After placing the Train Clustering
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he Metric parameter offers two opti
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the results of our K-means clusteri
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Train Clustering Model module remai
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Removes the Train Clustering Model,
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FIGURE 6-24 The wholesale customers
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FIGURE 6-26 The API help sample cod
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FIGURE 6-28 The CSV output file ope
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Chapter 7The Azure ML Matchboxrecom
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Back in the good old days of video
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webpages, recipes, or even other pe
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FIGURE 7-1 The restaurant ratings s
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FIGURE 7-2 Populating the three res
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Now it’s time to add the Train Ma
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Armed with this knowledge, let’s
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The last module to add to our Match
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prediction model will return a set
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FIGURE 7-17 The completed experimen
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Chapter 8Retraining Azure ML models
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Figure 8-2 illustrates the two appr
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Once you have re-created or located
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as a web service. After processing,
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After adding the three additional W
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FIGURE 8-9 Azure Machine Learning W
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FIGURE 8-11 A sample Azure Machine
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FIGURE 8-13 The two endpoints for t
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ResourcesFor more information about
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