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

Cleaning up

Now that we have completed working on the hands-on solutions of this chapter, it is time we clean up and turn off the resources we will no longer use. At this point in time, we have an EKS cluster running with 5 x m5.xlarge instances running. We need to terminate these resources to manage the cost.

Note

If we do not turn these off (for a month), how much would it cost? At a minimum (per month), it would cost around USD 700.80 for the running EC2 instances (5 instances x 0.192 USD x 730 hours in a month) plus USD 73 for the EKS cluster (1 Cluster x 0.10 USD per hour x 730 hours per month) assuming that we are running the EKS cluster in the Oregon region (us-west-2). Note that there will be other additional costs associated with the EBS volumes attached to these instances along with the other resources used in this chapter.

In the next set of steps, we will uninstall and delete the resources in the Cloud9 environment’s Terminal:

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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

Cleaning up

Authors (1)

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Table of Contents (19) Chapters

Cleaning up

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Authors (1)

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