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 Explainable Monitoring Framework

In this section, we will explore the Explainable Monitoring Framework (as shown in the following diagram) in detail to understand and learn how Explainable Monitoring enhances the MLOps workflow and the ML system itself:

Figure 11.6 – Explainable Monitoring Framework

The Explainable Monitoring Framework is a modular framework that's used to monitor, analyze, and govern a ML system while enabling continual learning. All the modules work in sync to enable transparent and Explainable Monitoring. Let's look at how each module works to understand how they contribute and function in the framework. First, let's look at the monitor module (the first panel in the preceding diagram).

Monitor

The monitor module is dedicated to monitoring the application in production (serving the ML model). Several factors are at play in an ML system, such as application performance (telemetry data, throughput...