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Engineering MLOps

You're reading from   Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Length 370 pages
Edition 1st Edition
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Author (1):
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Emmanuel Raj Emmanuel Raj
Author Profile Icon Emmanuel Raj
Emmanuel Raj
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Framework for Building Machine Learning Models
2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER 3. Chapter 2: Characterizing Your Machine Learning Problem 4. Chapter 3: Code Meets Data 5. Chapter 4: Machine Learning Pipelines 6. Chapter 5: Model Evaluation and Packaging 7. Section 2: Deploying Machine Learning Models at Scale
8. Chapter 6: Key Principles for Deploying Your ML System 9. Chapter 7: Building Robust CI/CD Pipelines 10. Chapter 8: APIs and Microservice Management 11. Chapter 9: Testing and Securing Your ML Solution 12. Chapter 10: Essentials of Production Release 13. Section 3: Monitoring Machine Learning Models in Production
14. Chapter 11: Key Principles for Monitoring Your ML System 15. Chapter 12: Model Serving and Monitoring 16. Chapter 13: Governing the ML System for Continual Learning 17. Other Books You May Enjoy

Testing our production-ready pipeline

Congratulations on setting up the production pipeline! Next, we will test its robustness. One great way to do this is to create a new release and observe and study whether the production pipeline successfully deploys the model to production (in the production Kubernetes cluster setup containing the pipeline). Follow these steps to test the pipeline:

  1. First, create a new release, go to the Pipelines | Releases section, select your previously created pipeline (for example, Port Weather ML Pipeline), and click on the Create Release button at the top right-hand side of the screen to initiate a new release, as shown here:

    Figure 10.11 – Create a new release

  2. Select the artifacts you would like to deploy in the pipeline (for example, Learn_MLOps repo, _scaler, and support-vector-classifier model and select their versions. Version 1 is recommended for testing PROD deployments for the first time), and click on the Create button at the top...
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