Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence
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Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies
Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description
Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way.
The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure.
By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for
If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
Table of ContentsAI Cloud FoundationsData Science Process Cognitive ServicesBot FrameworkAzure Machine Learning Studio Scalable Computing for Data ScienceMachine Learning Server HDInsight Machine Learning with Spark Building Deep Learning SolutionsIntegration with Other Azure Services An End to End Example
ASIN : B07FDBJ18K
Publisher : Packt Publishing; 1st edition (October 31, 2018)
Publication date : October 31, 2018
Language : English
File size : 33138 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 340 pages
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3 reviews for Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence
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Michael L. Friscia –
It’s current but Azure has changed since it was written
I attended Live AI in Orlando in December 2018, shortly after this book came out, and the book was a great refresher on doing many things I had forgotten how to do. The only trouble is that Azure has changed their interface a little and added many new features. So some of the screen shots in this book are outdated but the information is accurate. They also make references to tutorials, they still exist in the Azure GitHub but not in the same way in Azure portal as the book references.The problem with Machine Learning is that there are too many different aspects to cover. I think this book does a great job at touching on most of them. But for me, I wanted something to help navigate the Azure tools to give me a kick in the right direction. I know Python, I’ve worked with R and I’ve done a handful of tensorflow stuff on Linux docker images. But my work is entirely in Azure.If you are new to Azure and Machine Learning, I think this book is good. It summarizes a lot of what you can find in the Microsoft tutorials online for getting started with Azure ML studio and puts it into easy to follow steps. But be forewarned that Azure changes often, the concepts in this book remain solid but in another year, most of the steps will likely be much different. The change between December and now is pretty big in that Azure has added numerous new features to their AI/ML offerings. Having worked in Azure for a couple of years, I know in 6 months it will be even more different which will probably make this book obsolete by the summer of 2020 except for advanced Azure users that can translate this book to the newer Azure interface.I do recommend this book but keep i mind, it is exactly what the title says it is. It is not a book to teach how to do machine learning, it is a hands on book using microsoft experiments published in GitHub for use on the Azure platform that will help you understand how to use Azure tools for machine learning.
Md Abu S. Chowdhury –
Very poor Graphics Contents
Very poor Graphics Contents.. Unable to read many image contents
Colin –
For the price I was expecting something far more in-depth. It’s a big book but most of it is just screenshots, and much of the rest is just blurb/ML hype. Terminology isn’t explained and the authors skim over most detail – eg we are informed that R code can be embedded into ML studio to enable you to create bespoke ML algorithms, but there is no detail on how to actually go about doing this. Similarly there is a lack of detail on the other available functions in ML studio. Also, whilst there is a whole chapter on the data science lifecycle process, there is little detail on the application of this – ie what I, as a data scientist who is new to Azure, need to do to get from raw data in a lake to a fully deployed embedded ML solution.