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

Follow these steps to locate and turn off any remaining running instances in SageMaker Studio:

Click the Running Instances and Kernels icon in the sidebar of Amazon SageMaker Studio, as highlighted in the following screenshot:

Figure 6.34 – Turning off any remaining running instances

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

Turn off any remaining running instances under RUNNING INSTANCES by clicking the Shutdown button for each of the instances as highlighted in the preceding screenshot. Clicking the Shutdown button will open a pop-up window verifying the instance shutdown operation. Click the Shut down all button to proceed.

Note that this cleanup operation needs to be performed after using SageMaker Studio. These resources are not turned off automatically by SageMaker, even during periods of inactivity. Turning off unused...

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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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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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