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

Chapter 8: APIs and Microservice Management

In this chapter, you will learn about APIs and microservice management. So far, we have deployed ML applications that are served as APIs. Now we will look into how to develop, organize, manage, and serve APIs. You will learn the principles of API and microservice design for ML inference so that you can design your own custom ML solutions.

In this chapter, we will learn by doing as we build a microservice using FastAPI and Docker and serve it as an API. For this, we will go through the fundamentals of designing an API and microservice for an ML model trained previously (in Chapter 4, Machine Learning Pipelines). Lastly, we will reflect on some key principles, challenges, and tips to design a robust and scalable microservice and API for test and production environments. The following topics will be covered in this chapter:

Introduction to APIs and microservices
The need for microservices for ML
Old is gold – REST...

The rest of the chapter is locked

You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Chapter 8: APIs and Microservice Management

Authors (1)

Other recommended products

Personalised recommendations for you

You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Chapter 8: APIs and Microservice Management

Unlock this book and the full library FREE for 7 days

Authors (1)

Other recommended products

Personalised recommendations for you