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Learn Amazon SageMaker

You're reading from   Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800208919
Length 490 pages
Edition 1st Edition
Languages
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Author (1):
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Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introduction to Amazon SageMaker FREE CHAPTER 3. Chapter 2: Handling Data Preparation Techniques 4. Section 2: Building and Training Models
5. Chapter 3: AutoML with Amazon SageMaker Autopilot 6. Chapter 4: Training Machine Learning Models 7. Chapter 5: Training Computer Vision Models 8. Chapter 6: Training Natural Language Processing Models 9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks 10. Chapter 8: Using Your Algorithms and Code 11. Section 3: Diving Deeper on Training
12. Chapter 9: Scaling Your Training Jobs 13. Chapter 10: Advanced Training Techniques 14. Section 4: Managing Models in Production
15. Chapter 11: Deploying Machine Learning Models 16. Chapter 12: Automating Machine Learning Workflows 17. Chapter 13: Optimizing Prediction Cost and Performance 18. Other Books You May Enjoy

Chapter 10: Advanced Training Techniques

In the previous chapter, you learned when and how to scale training jobs using features such as Pipe Mode and distributed training, as well as alternatives to S3.

In this chapter, we'll conclude our exploration of training techniques. You'll learn how to slash down your training costs with Managed Spot Training, how to squeeze every drop of accuracy from your models with Automatic Model Tuning, and how to crack models open with SageMaker Debugger.

This chapter covers the following topics:

  • Optimizing training costs with Managed Spot Training
  • Optimizing hyperparameters with Automatic Model Tuning
  • Exploring models with SageMaker Debugger
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