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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Basic concepts of Amazon SageMaker Model Monitor and Amazon SageMaker Clarify

In this section, let's review the capabilities provided by two SageMaker features: Model Monitor and Clarify.

Amazon SageMaker Model Monitor provides capabilities to monitor data drift and the model quality of models deployed as SageMaker real-time endpoints. Amazon SageMaker Clarify provides capabilities to monitor the deployed model for bias and feature attribution drift. Using a combination of these two features, you can monitor the following four different aspects of ML models deployed on SageMaker:

Data drift: If the live inference traffic data served by the deployed model is statistically different from the training data the model was trained on, the model prediction accuracy will start to deteriorate. Using a combination of a training data baseline and periodic monitoring to compare the incoming inference requests with the baseline data, SageMaker Model Monitor detects data drift...