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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Using a model registry

A model registry allows you to centrally track key metadata for each model version. The granularity of metadata tracked is often dependent on the chosen implementation (Amazon SageMaker's model registry, a custom solution, or a third-party solution).

Regardless of the implementation, the key metadata to consider includes model version identifiers, and the following information about each model version registered:

Model inputs: These include metadata related to the inputs and versions of those inputs used to train the model. This can include inputs such as the name of the Amazon S3 bucket storing the training data, training hyperparameters, and the Amazon Elastic Container Registry (ECR) repository or container image used for training.
Model performance: This includes model evaluation data such as training and validation metrics.
Model artifact: This includes metadata about the training model artifact. At a minimum, this includes...