Book description
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.
Table of contents
- Cover
- Acknowledgments
- About the Author
- About the Technical Editor
- Introduction
- Part 1: Fundamentals of Machine Learning
-
Part 2: Machine Learning with Amazon Web Services
- Chapter 6: Introduction to Amazon Web Services
- Chapter 7: AWS Global Infrastructure
- Chapter 8: Identity and Access Management
- Chapter 9: Amazon S3
- Chapter 10: Amazon Cognito
- Chapter 11: Amazon DynamoDB
- Chapter 12: AWS Lambda
- Chapter 13: Amazon Comprehend
- Chapter 14: Amazon Lex
- Chapter 15: Amazon Machine Learning
-
Chapter 16: Amazon SageMaker
- Key Concepts
- Creating an Amazon SageMaker Notebook Instance
- Preparing Test and Training Data
- Training a Scikit-Learn Model on an Amazon SageMaker Notebook Instance
- Training a Scikit-Learn Model on a Dedicated Training Instance
- Training a Model Using a Built-in Algorithm on a Dedicated Training Instance
- Summary
- Chapter 17: Using Google TensorFlow with Amazon SageMaker
- Chapter 18: Amazon Rekognition
- Appendix A: Anaconda and Jupyter Notebook Setup
- Appendix B: AWS Resources Needed to Use This Book
- Appendix C: Installing and Configuring the AWS CLI
- Appendix D: Introduction to NumPy and Pandas
- Index
- End User License Agreement
Product information
- Title: Machine Learning in the AWS Cloud
- Author(s):
- Release date: September 2019
- Publisher(s): Sybex
- ISBN: 9781119556718
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