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

View More author details

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

Testing your ML solution by design

On top of performing regular software development tests, such as unit tests, integration tests, system testing, and acceptance testing, ML solutions need additional tests because data and ML models are involved. Both the data and models change dynamically over time. Here are some concepts for testing by design; applying them to your use cases can ensure robust ML solutions are produced as a result.

Data testing

The goal of testing data is to ensure that the data is of a high enough quality for ML model training. The better the quality of the data, the better the models trained for the given tasks. So how do we assess the quality of data? It can be done by inspecting the following five factors of the data: