You're reading from Engineering MLOps

Product typeBook

Published inApr 2021

PublisherPackt

ISBN-139781800562882

Edition1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1)

Emmanuel Raj

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...

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Engineering MLOps

Published in: Apr 2021Publisher: PacktISBN-13: 9781800562882

Author (1)

Emmanuel Raj

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.
Read more about Emmanuel Raj

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