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

Selecting an appropriate deployment strategy

As you have seen so far, the initial deployment of a machine model is only one step of making it available to consumers. New versions of models are built regularly. Before making the new models available to the consumers, the model quality and infrastructure that's needed to host the model should be evaluated carefully. There are multiple factors to consider when selecting the deployment strategy to initially deploy and continue to update models. For example, not all models can be tested in production due to budget and resource constraints. Similarly, some model consumers can tolerate the model being unavailable for certain periods.

This section will summarize the deployment strategies you can use to deploy and update real-time SageMaker Endpoints. You will get an idea of the pros and cons for each strategy, in addition to when should it be used.