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

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Automating with AWS CloudFormation

AWS CloudFormation has long been the preferred way to automate infrastructure builds and operations on AWS (https://aws.amazon.com/cloudformation). We could certainly write a book on the topic, but we'll stick to basics in this section.

The first step in using CloudFormation is to write a template, a JSON or YAML text file describing the resources that you want to build, such as an EC2 instance or an S3 bucket. Resources are available for almost all AWS services, and SageMaker is no exception.If we look at https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/AWS_SageMaker.html, we see that we can create Notebook instances and deploy endpoints.

A template can (and should) include parameters and outputs. The former help make templates as generic as possible. The latter provide information that can be used by downstream applications, such as instance DNS names or bucket names.

Once you've written your template file, you pass...