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

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

View More author details

Table of Contents (19) Chapters

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

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Chapter 8: Using Your Algorithms and Code

In the previous chapter, you learned how to train and deploy models with built-in frameworks such as scikit-learn or TensorFlow. Thanks to Script Mode, these frameworks make it easy to use your own code, without having to manage any training or deployment containers.

In some cases, your business or technical environment could make it difficult or even impossible to use these containers. Maybe you need to be in full control of how containers are built. Maybe you'd like to implement your own prediction logic. Maybe you're working with a framework or a language that's not natively supported by SageMaker.

In this chapter, you'll learn how to tailor training and prediction containers to your own needs. You'll also learn how to train and deploy your own custom code, using either the SageMaker SDK directly or command-line open source tools. We'll cover the following topics: