Complete Hands-On Guide To FastAPI With Machine Learning Deployment

Complete Hands-On Guide To FastAPI With Machine Learning Deployment

Complete Hands-On Guide To FastAPI With Machine Learning Deployment. In this article, we will discuss the implementation of FastAPI and demonstrate how it can be used for deployment purposes.

Any machine learning model’s end goal is a deployment for production purposes. Building a REST API(Application Programming Interface) is the best possible way to evaluate model performance. In python, Django and more evidently Flask frameworks are used for this purpose. For machine learning, Flask is preferred more than Django. Here comes FastAPI which is faster than Flask, providing higher performance boost, easier to code, comes with automatic documentation, provides data validation on input data provided to the server, and many more such amazing features which I’ll be covering in this article.

FastAPI is one of the fastest python frameworks available. The automatic documentation comes with free of cost. Since it is based on python so it provides nice python type hints for autocompletion and type checks. FastAPI is built upon two major python libraries – Starlette(for web handling) and Pydantic(for data handling). FastAPI is based on some standard integrations — OpenAPI, JSON Schema, OAuth2.

In this article, we will discuss the implementation of FastAPI and demonstrate how it can be used for deployment purposes.


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