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Engineering MLOps

You're reading from  Engineering MLOps

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Emmanuel Raj Emmanuel Raj
Profile icon Emmanuel Raj

Table of Contents (18) Chapters

Preface 1. Section 1: Framework for Building Machine Learning Models
2. Chapter 1: Fundamentals of an MLOps Workflow 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

Configuring pipeline triggers for automation

In this section, we will configure three triggers based on artifacts that we have already connected to the pipeline. The triggers we will set up are as follows:

  • Git trigger: For making code changes to the master branch.
  • Artifactory trigger: For when a new model or artifact is created or trained.
  • Schedule trigger: A weekly periodic trigger.

Let's look at each of these pipeline triggers in detail.

Setting up a Git trigger

In teams, it is common to set a trigger for deployment when code changes are made to a certain branch in the repository. For example, when code changes are made to the master branch or the develop branch, CI/CD pipelines are triggered to deploy the application to the PROD or DEV TEST environments, respectively. When a pull request is made to merge code in the master or develop branch, the QA expert or product manager accepts the pull request in order to merge with the respective branch...

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