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Machine Learning Engineering on AWS

You're reading from   Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247595
Length 530 pages
Edition 1st Edition
Tools
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

Testing our ML inference endpoint

Of course, we need to check whether the ML inference endpoint is working! In the next set of steps, we will download and run a Jupyter notebook (named Test Endpoint and then Delete.ipynb) that tests our ML inference endpoint using the test dataset:

  1. Let’s begin by opening the following link in another browser tab: https://bit.ly/3xyVAXz
  2. Right-click on any part of the page to open a context menu, and then choose Save as... from the list of available options. Save the file as Test Endpoint then Delete.ipynb, and then download it to the Downloads folder (or similar) on your local machine.
  3. Navigate back to your SageMaker Studio environment. In the File Tree (located on the left-hand side of the SageMaker Studio environment), make sure that you are in the CH11 folder similar to what we have in Figure 11.15:

Figure 11.15 – Uploading the test endpoint and then the Delete.ipynb file

  1. Click on the...
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