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

Understanding the need for continual learning

When we got started in Chapter 1, Fundamentals of MLOps Workflow, we learned about the reasons AI adoption is stunted in organizations. One of the reasons was the lack of continual learning in ML systems. Yes, continual learning! We will address this challenge in this chapter and make sure we learn how to enable this capability by the end of this chapter. Now, let's look into continual learning.

Continual learning

Continual learning is built on the principle of continuously learning from data, human experts, and the external environment. Continual learning enables lifelong learning, with adaptation at its core. It enables ML systems to become intelligent over time to adapt to the task at hand. It does this by monitoring and learning from the environment and the human experts assisting the ML system. Continual learning can be a powerful add-on to an ML system. It can allow you to realize the maximum potential of an AI system...