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

Pipeline release management

Releases in the CI/CD pipelines allow your team to automate fully and continuously deliver software to your customers faster and with lower risk. Releases allow you to test and deliver your software in multiple stages of production or set up semi-automated processes with approvals and on-demand deployments. It is vital to monitor and manage these releases. We can manage releases by accessing the pipeline from Pipelines | Releases and selecting our CI/CD pipeline (for example, Port Weather ML Pipeline), as shown in the following screenshot:

Figure 10.16 – Pipeline Release Management

Here, you can keep track of all the releases and their history and perform operations for each release, such as redeploying, abandoning, checking logs, and so on. You can see the releases shown in the following screenshot. By clicking on individual releases (for example, Release 4), we can check which model and artifacts were deployed in the release...

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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

Pipeline release management

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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

Pipeline release management

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