Machine Learning in the AWS Cloud

Machine Learning in the AWS Cloud Add Intelligence to Applications With Amazon SageMaker and Amazon Rekognition

Paperback (08 Oct 2019)

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Publisher's Synopsis

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! 

Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.

Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.

    Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building

    Discover common neural network frameworks with Amazon SageMaker

    Solve computer vision problems with Amazon Rekognition

    Benefit from illustrations, source code examples, and sidebars in each chapter

The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.

Book information

ISBN: 9781119556718
Publisher: Wiley
Imprint: Sybex
Pub date:
DEWEY: 005.8
DEWEY edition: 23
Language: English
Number of pages: xxvii, 499
Weight: 912g
Height: 188mm
Width: 234mm
Spine width: 38mm