Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
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
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

Arrow left icon
Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801817950
Length 554 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introducing 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 CV 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 into 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 4: Training Machine Learning Models

In the previous chapter, you learned how Amazon SageMaker Autopilot makes it easy to build, train, and optimize models automatically, without writing a line of machine learning code.

For problem types that are not supported by SageMaker Autopilot, the next best option is to use one of the algorithms already implemented in SageMaker and to train it on your dataset. These algorithms are referred to as built-in algorithms, and they cover many typical machine learning problems, from classification to time series to anomaly detection.

In this chapter, you will learn about built-in algorithms for supervised and unsupervised learning, what type of problems you can solve with them, and how to use them with the SageMaker SDK:

  • Discovering the built-in algorithms in Amazon SageMaker
  • Training and deploying models with built-in algorithms
  • Using the SageMaker SDK with built-in algorithms
  • Working with more built-in algorithms
  • ...
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