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

Old is gold – REST API-based microservices

Old is gold. Plus, it's better to start somewhere where there are various API protocols. The Representational State Transfer (REST) protocol has become a gold standard for many applications over the years, and it's not so very different for ML applications today. The majority of companies prefer developing their ML applications based on the REST API protocol.

A REST API or RESTful API is based on REST, an architectural method used to communicate mainly in web services development.

RESTful APIs are widely used; companies such as Amazon, Google, LinkedIn, and Twitter use them. Serving our ML models via RESTful APIs has many benefits, such as the following: