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

Amazon SageMaker Debugger essentials

In this section, you will learn about the basic terminology and capabilities of Amazon SageMaker Debugger. Using Debugger with your training jobs involves three high-level steps:

Configuring the training job to use SageMaker Debugger.
Analyzing the collected tensors and metrics.
Taking action.

The preceding points are illustrated in the following diagram:

Figure 7.1 – Amazon SageMaker Debugger overview

As we dive into each one of these steps, we will introduce the necessary terminology.

Configuring a training job to use SageMaker Debugger

The first step is to configure training jobs to use Amazon SageMaker Debugger. By now, you are familiar with using the Estimator object from SageMaker SDK to launch training jobs. To use Amazon SageMaker Debugger, you must enhance Estimator with three additional configuration parameters: DebuggerHookConfig, Rules, and ProfilerConfig.

With...