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

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

References

For additional reading material, please review these references:

Using streaming ingestion with Amazon SageMaker Feature Store to make ML-backed decisions in near-real time:
https://aws.amazon.com/blogs/machine-learning/using-streaming-ingestion-with-amazon-sagemaker-feature-store-to-make-ml-backed-decisions-in-near-real-time/
Enable feature reuse across accounts and teams using Amazon SageMaker Feature Store:
https://aws.amazon.com/blogs/machine-learning/enable-feature-reuse-across-accounts-and-teams-using-amazon-sagemaker-feature-store/
Build accurate ML training datasets using point-in-time queries with Amazon SageMaker Feature Store and Apache Spark:
https://aws.amazon.com/blogs/machine-learning/build-accurate-ml-training-datasets-using-point-in-time-queries-with-amazon-sagemaker-feature-store-and-apache-spark/
Ingesting historical feature data into Amazon SageMaker Feature Store:
https://towardsdatascience.com/ingesting-historical-feature-data-into...