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

Maintaining the CI/CD pipeline

As you may recall, in Chapter 10, Essentials of Production Release, we mentioned that a model is not the product; the pipeline is the product. Hence, after setting up automated or semi-automated CI/CD pipelines, it is critical to monitor the performance of our pipeline. We can do that by inspecting the releases in Azure DevOps, as shown in the following screenshot:

Figure 13.18 – Maintaining CI/CD pipeline releases

The goal of an inspection is to keep the CI/CD pipeline in a healthy and robust state. Here are some guidelines for keeping the CI/CD pipeline healthy and robust:

If a build is broken, a fix it asap policy from the team should be implemented.
Integrate automated acceptance tests.
Require pull requests.
Peer code review each story or feature.
Audit system logs and events periodically (recommended).
Regularly report metrics visibly to all the team members (for example, slackbot or...

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

Table of Contents (18) Chapters

Maintaining the CI/CD pipeline

Authors (1)

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

Table of Contents (18) Chapters

Maintaining the CI/CD pipeline

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