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

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

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

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...