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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Exploring Amazon SageMaker Processing

Collecting and labeling data samples is only the first step in preparing a dataset. Indeed, it's very likely that you'll have to pre-process your dataset in order to do the following, for example:

Convert it to the input format expected by the machine learning algorithm you're using.
Rescale or normalize numerical features.
Engineer higher-level features, for example, one-hot encoding.
Clean and tokenize text for natural language processing applications.
And more!

Once training is complete, you may want to run additional jobs to post-process the predicted data and to evaluate your model on different datasets.

In this section, you'll learn about Amazon SageMaker Processing, a SageMaker capability that helps you run batch jobs related to your machine learning project.