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

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18. Other Books You May Enjoy

Preparing the essential prerequisites

In this section, we will ensure that the following prerequisites are ready before proceeding with setting up our data warehouse and data lake in this chapter:

A text editor (for example, VS Code) on your local machine
An IAM user with the permissions to create and manage the resources we will use in this chapter
A VPC where we will launch the Redshift Serverless endpoint
A new S3 bucket where our data will be uploaded using AWS CloudShell

In this chapter, we will create and manage our resources in the Oregon (us-west-2) region. Make sure that you have set the correct region before proceeding with the next steps.

Opening a text editor on your local machine

Make sure you have an open text editor (for example, VS Code) on your local machine. We will copy some string values into the text editor for later use in this chapter. Here are the values we will have to copy later in this chapter:

IAM sign-in link, username...

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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

Preparing the essential prerequisites

Opening a text editor on your local machine

Authors (1)

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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

Preparing the essential prerequisites

Opening a text editor on your local machine

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Authors (1)

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