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

Summary

In this chapter, we were able to take a closer look at AWS Deep Learning Containers (DLCs). Similar to AWS Deep Learning AMIs (DLAMIs), AWS DLCs already have the relevant ML frameworks, libraries, and packages installed. This significantly speeds up the process of building and deploying deep learning models. At the same time, container environments are guaranteed to be consistent since these are run from pre-built container images.

One of the key differences between DLAMIs and DLCs is that multiple AWS DLCs can run inside a single EC2 instance. These containers can also be used in other AWS services that support containers. These services include AWS Lambda, Amazon ECS, Amazon EKS, and Amazon EC2, to name a few.

In this chapter, we were able to train a deep learning model using a DLC. We then deployed this model to an AWS Lambda function through Lambda’s container image support. After that, we tested the Lambda function to see whether it’s able to successfully...

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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

Summary

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Table of Contents (19) Chapters

Summary

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