What do you like best about being a data scientist? It’s definitely modeling and fine-tuning for optimal results. But what does it mean to be a good model if it’s never used or never deployed?

To produce a machine learning model, the typical approach is to wrap it in a REST API and use it as a microservice. One of the most widely used frameworks for creating APIs is Flask.

The main reason Flask is widely used is its simplicity. In general, we only use the API to model predictions, so we don’t need a complex architecture (example: Django). Another reason is that Flask is written in Python, which is the language used to do machine learning modeling in general, so we are familiar with it.

However, if you want to create a REST API with clear, static, inputs that are validated, you must include several different packages from several third parties that do not cooperate with each other. And you have to create custom code to get everything running.

This is what caused me to look for alternatives for my needs, where finally I found a framework called FastAPI and it became my new favorite framework. Here are the reasons why I like to use FastAPI.

#machine-learning #python #flask #web-development