You're reading from Automated Machine Learning on AWS Fast-track the development of your production-ready machine learning applications the AWS way

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801811828

Length 420 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1):

Trenton Potgieter

Preface

1. Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS

2. Chapter 1: Getting Started with Automated Machine Learning on AWS FREE CHAPTER

3. Chapter 2: Automating Machine Learning Model Development Using SageMaker Autopilot

4. Chapter 3: Automating Complicated Model Development with AutoGluon

5. Section 2: Automating the Machine Learning Process with Continuous Integration and Continuous Delivery (CI/CD)

6. Chapter 4: Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning

7. Chapter 5: Continuous Deployment of a Production ML Model

8. Section 3: Optimizing a Source Code-Centric Approach to Automated Machine Learning

9. Chapter 6: Automating the Machine Learning Process Using AWS Step Functions

10. Chapter 7: Building the ML Workflow Using AWS Step Functions

11. Section 4: Optimizing a Data-Centric Approach to Automated Machine Learning

12. Chapter 8: Automating the Machine Learning Process Using Apache Airflow

13. Chapter 9: Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow

14. Section 5: Automating the End-to-End Production Application on AWS

15. Chapter 10: An Introduction to the Machine Learning Software Development Life Cycle (MLSDLC)

16. Chapter 11: Continuous Integration, Deployment, and Training for the MLSDLC

17. Other Books You May Enjoy

Technical requirements

To follow along with the code examples in this chapter, you will need the following:

Web browser (for the best experience, it is recommended that you use Chrome or Firefox browsers).
Access to the AWS account that we used in Chapter 6, Automating the Machine Learning Process Using AWS Step Functions.
Access to the Cloud9 development environment we used in Chapter 6, Automating the Machine Learning Process Using AWS Step Functions.
We will once again be working within the usage limits of the AWS Free Tier to avoid incurring unnecessary costs.
Access to the SageMaker Studio environment we created in Chapter 2, Automating Machine Learning Model Development Using SageMaker Autopilot.
Source code examples and Jupyter Notebooks are provided in the companion GitHub repository for this chapter (https://github.com/PacktPublishing/Automated-Machine-Learning-on-AWS/tree/main/Chapter07). The code examples should already be available in the Cloud9...

The rest of the chapter is locked