Serving the models with FastAPI
The simplest and potentially most flexible approach to serving ML models in a microservice with Python is in wrapping the serving logic inside a lightweight web application. Flask has been a popular option among Python users for many years but now the FastAPI web framework has many advantages, which means it should be seriously considered as a better alternative.
Some of the features of FastAPI that make it an excellent choice for a lightweight microservice are:
- Data validation: FastAPI uses and is based on the Pydantic library, which allows you to enforce type hints at runtime. This allows for the implementation of very easy-to-create data validation steps that make your system way more robust and helps avoid edge case behaviors.
- Built-in async workflows: FastAPI gives you asynchronous task management out of the box with
asyncandawaitkeywords, so you can build the logic you will need in many cases relatively seamlessly without...
