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

Best practices for monitoring ML models

This section discusses best practices for monitoring models using SageMaker Model Monitor and SageMaker Clarify, taking into consideration the under-the-hood operation of these features and a few limitations as they stand at the time of publication of this book:

Choosing the correct data format: Model Monitor and Clarify can only monitor for drift in tabular data. Therefore, ensure that your training data is in tabular format. For other data formats, you will have to build custom monitoring containers.
Choosing real-time endpoints as the mode of model deployment: Model Monitor and Clarify support monitoring for a single-model real-time endpoint. Monitoring a model used with batch transform or multi-model endpoints is not supported. So, ensure that the model you want to monitor is deployed as a single-model real-time endpoint. Additionally, if the model is part of an inference pipeline, the entire pipeline is monitored, not the individual...