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 for us to clean up and turn off any resources we will no longer use. In the next set of steps, we will locate and turn off any remaining running instances in SageMaker Studio:

Click the Running Instances and Kernels icon in the sidebar, as highlighted in Figure 7.21:

Figure 7.21 – Turning off the running instance

Clicking the Running Instances and Kernels icon should open and show the running instances, apps, and terminals in SageMaker Studio.

Turn off all running instances under RUNNING INSTANCES by clicking the Shut down button for each of the instances, as highlighted in Figure 7.21. Clicking the Shut down button will open a pop-up window verifying the instance shutdown operation. Click the Shut down all button to proceed.
Make sure to check for and delete all the running inference endpoints under SageMaker resources as well...

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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

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Table of Contents (19) Chapters

Cleaning up

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