You're reading from Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803247595

Length 530 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

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Table of Contents (19) Chapters

Preface

1. Part 1: Getting Started with Machine Learning Engineering on AWS

2. Chapter 1: Introduction to ML Engineering on AWS FREE CHAPTER

3. Chapter 2: Deep Learning AMIs

4. Chapter 3: Deep Learning Containers

5. Part 2:Solving Data Engineering and Analysis Requirements

6. Chapter 4: Serverless Data Management on AWS

7. Chapter 5: Pragmatic Data Processing and Analysis

8. Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions

9. Chapter 6: SageMaker Training and Debugging Solutions

10. Chapter 7: SageMaker Deployment Solutions

11. Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments

12. Chapter 8: Model Monitoring and Management Solutions

13. Chapter 9: Security, Governance, and Compliance Strategies

14. Part 5:Designing and Building End-to-end MLOps Pipelines

15. Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS

16. Chapter 11: Machine Learning Pipelines with SageMaker Pipelines

17. Index

Why subscribe?

18. Other Books You May Enjoy

Technical requirements

Before we start, it is important that we have the following ready:

A web browser (preferably Chrome or Firefox)
Access to the AWS account used in the first four chapters of the book

The Jupyter notebooks, source code, and other files used for each chapter are available in this repository: https://github.com/PacktPublishing/Machine-Learning-Engineering-on-AWS.

Important Note

Make sure to sign out and NOT use the IAM user created in Chapter 4, Serverless Data Management on AWS. In this chapter, you should use the root account or a new IAM user with a set of permissions to create and manage the AWS Glue DataBrew, Amazon S3, AWS CloudShell, and Amazon SageMaker resources. It is recommended to use an IAM user with limited permissions instead of the root account when running the examples in this book. We will discuss this along with other security best practices in further detail in Chapter 9, Security, Governance, and Compliance Strategies...

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You're reading from Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Table of Contents (19) Chapters

Technical requirements

Authors (1)

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Table of Contents (19) Chapters

Technical requirements

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