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

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Best practices for cost-optimized ML workloads

For many organizations, the lost opportunity cost of not embracing disruptive technologies such as ML outweighs the ML costs. By implementing a few best practices, these organizations can get the best possible returns on their ML investment. In this section, we will discuss best practices to apply for cost-optimized ML workloads on SageMaker.

Let's now look at best practices for building cost-optimized ML workloads on AWS in the following sections.

Optimizing data labeling costs

Labeling of data used for ML training, typically done at the very beginning of the ML process, can be tedious, error-prone, and time-consuming. Labeling at scale consumes many working hours, making this an expensive task, too. To optimize cost for data labeling, use SageMaker Ground Truth. Ground Truth provides capabilities for data labeling at scale using a combination of human workforce and active learning. When active learning is enabled, a labeling...