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

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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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Technical requirements

You will need an AWS account to run the examples included in this chapter. If you haven't got one already, please point your browser to https://aws.amazon.com/getting-started/ to create one. You should also familiarize yourself with the AWS Free Tier (https://aws.amazon.com/free/), which lets you use many AWS services for free within certain usage limits.

You will need to install and configure the AWS Command-Line Interface for your account (https://aws.amazon.com/cli/).

You will need a working Python 3.x environment. Be careful not to use Python 2.7, as it is no longer maintained. Installing the Anaconda distribution (https://www.anaconda.com/) is not mandatory, but strongly encouraged, as it includes many projects that we will need (Jupyter, pandas, numpy, and more).

You will need a working Docker installation. You can find installation instructions and the necessary documentation at https://docs.docker.com.

The code examples included in...