Why FastAPI is the best web python framework for Data Scientists?
APIs (Application Programming Interfaces) are an important tool for data scientists and machine learning engineers. They allow us to easily access and work with data and services, and to build custom connections between different systems.
By building APIs, we can share our data and models with other applications, or use APIs provided by others to access data and functionality for our own projects. The right API framework can make building and using APIs quick and easy, making them a valuable part of any data science or machine learning workflow.
As a data scientist, I’ve had the opportunity to try out many different Python frameworks for building APIs. But after using FastAPI, I can confidently say that it is my absolute favorite.
One of the main reasons I love FastAPI is that it is incredibly fast and efficient, especially when dealing with a large amount of data or requests at the same time. This is thanks to the use of the modern asyncio library, which outclasses other libraries like WSGI in terms of handling multiple requests concurrently.
But speed isn’t the only benefit of FastAPI. It also has a simple codebase, making it easy to learn and use. This is especially important for me as a data scientist, as I don’t want to get bogged down in complicated code when working on machine learning projects.
In addition to its simplicity and speed, FastAPI has a ton of useful features. For instance, you can use Python’s built-in type hints to automatically validate the data sent to and from your API. This helps ensure that everything is working correctly and that your API is easy for other developers to use.
But the benefits of FastAPI don’t stop there. It also has an active community that is constantly working on new plugins and integrations to improve the framework. This is great for when I need to integrate my API with other tools or libraries.
Overall, FastAPI is an excellent choice for building APIs, particularly for data scientists and machine learning engineers like myself. It’s fast, efficient, easy to use, and has a lot of helpful features and integrations.
If you haven’t tried it yet, I highly recommend giving it a shot. Be sure to check out the official documentation and the GitHub repository for more information.
Subscribe to DDIntel Here.
Visit our website here: https://www.datadriveninvestor.com
Join our network here: https://datadriveninvestor.com/collaborate
