You're reading from Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801070522

Length 348 pages

Edition 1st Edition

Languages

Python

Tools

Amazon SimpleDB

Concepts

Machine Learning

Authors (3):

Randy DeFauw

Shelbee Eigenbrode

Sireesha Muppala

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Processing Data at Scale

2. Chapter 1: Amazon SageMaker Overview FREE CHAPTER

3. Chapter 2: Data Science Environments

4. Chapter 3: Data Labeling with Amazon SageMaker Ground Truth

5. Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing

6. Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store

7. Section 2: Model Training Challenges

8. Chapter 6: Training and Tuning at Scale

9. Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger

10. Section 3: Manage and Monitor Models

11. Chapter 8: Managing Models at Scale Using a Model Registry

12. Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants

13. Chapter 10: Optimizing Model Hosting and Inference Costs

14. Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify

15. Section 4: Automate and Operationalize Machine Learning

16. Chapter 12: Machine Learning Automated Workflows

17. Chapter 13:Well-Architected Machine Learning with Amazon SageMaker

18. Chapter 14: Managing SageMaker Features across Accounts

19. Other Books You May Enjoy

Section 1: Processing Data at Scale

This section sets the foundation for the rest of the book with an overview of Amazon SageMaker capabilities, a review of technical requirements, and insights on setting up the data science environment on AWS. This section then addresses the challenges involved in labeling and preparing large volumes of data. You will learn how to apply appropriate Amazon SageMaker capabilities and related services to derive features from raw data and persist features for reuse. Further, you will also learn how to persist features in a centralized repository to share across multiple ML projects.

This section comprises the following chapters:

Chapter 1, Amazon SageMaker Overview
Chapter 2, Data Science Environments
Chapter 3, Data Labeling with Amazon SageMaker Ground Truth
Chapter 4, Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
Chapter 5, Centralized Feature Repository with Amazon SageMaker Feature Store...