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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Creating CI/CD pipelines using Amazon SageMaker Projects

In this section, we'll discuss using Amazon SageMaker Projects to incorporate CI/CD practices into your ML pipelines. SageMaker Projects is a service that uses SageMaker Pipelines and the SageMaker model registry, in combination with CI/CD tools, to automatically provision and configure CI/CD pipelines for ML. Figure 12.10 illustrates  the core components of SageMaker Projects. With Projects, you have the advantage of a CD pipeline, source code versioning, and automatic triggers for pipeline execution:

Figure 12.10 – SageMaker Projects

Projects are made available through built-in SageMaker MLOps project templates or by creating your own organization's MLOps templates. The underlying templates are offered through AWS Service Catalog, via SageMaker Studio, and contain CloudFormation templates that preconfigure CI/CD pipelines for the selected template. Because projects rely on...