In this article, we are going to build a TensorFlow model (v2) and, using FastAPI, create REST API calls to predict from the model, and finally containerize it using Docker 😃
I want to emphasize the usage of FastAPI and how rapidly this framework is a game-changer for building easy to go and much faster API calls for a machine learning pipeline. Traditionally, we use Flask Microservices for building REST API calls but the process involves a bit of nitty-gritty to understand the framework and implement it. On the other end, I found FastAPI to be pretty user-friendly and very easy to pick up and implement type of framework.
And finally from one game-changer to another, Docker
As a data scientist: our role is vague and it keeps on changing year in year out. Some skillset gets added, some get extinct and obsolete, but Docker has made its mark as one of the very important and most sought out skills in the market. Docker gives us the ability to containerize a solution with all its binding software and requirements.

#rest-api #deep-learning #docker #tensorflow #fastapi