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

Getting started with Deep Learning AMIs

Before we talk about DLAMIs, we must have a good idea of what AMIs are. We can think of an AMI as the “DNA” of an organism. Using this analogy, the organism would correspond and map to one or more EC2 instances:

Figure 2.1 – Launching EC2 instances using Deep Learning AMIs

If we were to launch two EC2 instances using the same AMI (similar to what is shown in Figure 2.1), both instances would have the same set of installed packages, frameworks, tools, and operating systems upon instance launch. Of course, not everything needs to be the same as these instances may have different instance types, different security groups, and other configurable properties.

AMIs allow engineers to easily launch EC2 instances in consistent environments without having to spend hours installing different packages and tools. In addition to the installation steps, these EC2 instances need to be configured and optimized...

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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

Getting started with Deep Learning AMIs

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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

Getting started with Deep Learning AMIs

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