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

Introducing Amazon MWAA

Managing big data platforms isn't typically part of the ML practitioner's portfolio of tasks. Oftentimes, the ML practitioner and data engineering teams rely on the infrastructure and operations teams to manage these platforms.

Including Airflow as part of the big data infrastructure means that these platform teams must now manage additional compute resources, orchestrate their deployment, update software and operating system patches, and monitor these resources to ensure that they are constantly addressing workload scaling requirements and other Service-Level Agreements (SLAs).

AWS offers multiple big data managed services, such as EMR and Glue, to help offload these management tasks from the platform teams and, in November 2020, AWS launched Amazon MWAA to help offload the management of the Airflow platform. With MWAA, the platform teams can run a highly available and scalable Airflow cluster without having to individually provision, update...

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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

Introducing Amazon MWAA

Authors (1)

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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

Introducing Amazon MWAA

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