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

Hands-on implementation of serving an ML model as an API

In this section, we will apply the principles of APIs and microservices that we have learned previously (in the section Introduction to APIs and microservices) and develop a RESTful API service to serve the ML model. The ML model we'll serve will be for the business problem (weather prediction using ML) we worked on previously. We will use the FastAPI framework to serve the model as an API and Docker to containerize the API service into a microservice.

FastAPI is a framework for deploying ML models. It is easy and fast to code and enables high performance with features such as asynchronous calls and data integrity checks. FastAPI is easy to use and follows the OpenAPI Specification, making it easy to test and validate APIs. Find out more about FastAPI here: https://fastapi.tiangolo.com/.

API design and development

We will develop the API service and run it on a local computer. (This could also be developed on...

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

Hands-on implementation of serving an ML model as an API

API design and development

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

Hands-on implementation of serving an ML model as an API

API design and development

Unlock this book and the full library FREE for 7 days

Authors (1)

Other recommended products

Personalised recommendations for you