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

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Table of Contents (18) Chapters

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

Summary

In this chapter, we re-factored the Age Calculator example from Chapter 4, Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning, to further streamline the overall ML process by integrating the development teams and ML practitioner teams based on their areas of expertise.

For example, with this re-factored process, the development teams can now focus their expertise on building and developing the CI/CD components, while the ML practitioner teams can focus on codifying the ML process by using the Data Science SDK.

In the next chapter, we will switch personas to the ML practitioner team and review how they can codify the ML workflow as a Step Functions state machine.

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

Table of Contents (18) Chapters

Summary

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Table of Contents (18) Chapters

Summary

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