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 Nov 2021

Publisher Packt

ISBN-13 9781801817950

Length 554 pages

Edition 2nd 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: 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

Building end-to-end workflows with AWS Step Functions

AWS Step Functions let you define and run workflows based on state machines (https://aws.amazon.com/step-functions/). A state machine is a combination of steps, which can be sequential, parallel, or conditional. Each step receives an input from its predecessor, performs an operation, and passes the output to its successor. Step Functions are integrated with many AWS services, such as Amazon SageMaker, AWS Lambda, container services, Amazon DynamoDB, Amazon EMR, AWS Glue, and more.

State machines can be defined using JSON and the Amazon States Language, and you can visualize them in the service console. State machine execution is fully managed, so you don't need to provision any infrastructure to run.

When it comes to SageMaker, Step Functions has a dedicated Python SDK, oddly named the Data Science SDK (https://github.com/aws/aws-step-functions-data-science-sdk-python).

Let's run an example where we automate training...

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

Table of Contents (19) Chapters

Building end-to-end workflows with AWS Step Functions

Authors (1)

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

Table of Contents (19) Chapters

Building end-to-end workflows with AWS Step Functions

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