Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Amazon SageMaker Best Practices

You're reading from   Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Arrow left icon
Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070522
Length 348 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (3):
Arrow left icon
Randy DeFauw Randy DeFauw
Author Profile Icon Randy DeFauw
Randy DeFauw
Shelbee Eigenbrode Shelbee Eigenbrode
Author Profile Icon Shelbee Eigenbrode
Shelbee Eigenbrode
Sireesha Muppala Sireesha Muppala
Author Profile Icon Sireesha Muppala
Sireesha Muppala
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

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

Chapter 2: Data Science Environments

In this chapter, we will get an overview of how to create managed data science environments to scale and create repeatable environments for your model-building activities. In this chapter, you will get a brief overview of the machine learning (ML) use case, including the dataset that will be used throughout the chapters in this book.

The topics that will be covered in this chapter are as follows:

  • Machine learning use case and dataset
  • Creating data science environments
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime