fastapi VS BentoML

FastAPI is a modern, high-performance web framework for building APIs with Python 3.6+ based on standard Python type hints. It is designed for quick development and high efficiency. Zuplo is a proud sponsor of the FastAPI project, to help drive API development in Python.

For Small teams and Startups I would recommend picking an API framework that automatically generates an OpenAPI spec through your code. Be very careful in which framework you pick as many claim to have OpenAPI support, but in reality only support a few fields. Many recent frameworks are built from the ground up around OpenAPI support. My recommendations are Huma for Go, Tsoa for Typescript, and FastAPI for Python. Your developers will be primarily be responsible for keeping descriptions and summaries up to date, but at least you reduce the risk of having undocumented endpoints or out-of-date documentation.

FastAPI Documentation

FastAPI serves as our backend framework, chosen for several compelling reasons:

Spring Boot has simplified the development process to a simple one. For python, I recommend a third-party package for developing API interfaces: fastapi.

Source: https://github.com/fastapi/fastapi

Python is a great language for building various types of applications, especially in today's landscape where machine learning and AI are rapidly advancing. With this growth in services, there’s a strong need for well-designed, maintainable, and scalable APIs. That’s where FastAPI comes in, a powerful async web API framework for Python that's both simple and robust https://fastapi.tiangolo.com/.

FastAPI is an open-source, high-performance web framework for building APIs with Python 3.7+ based on standard Python type hints. It enables developers to build applications efficiently and quickly. FastAPI leverages Pydantic for type hinting and includes built-in API documentation.

For detailed information on how to get started with FastAPI, check out the FastAPI Documentation.

Form2Mail addresses these challenges by offering a self-hosted, flexible API that gives developers full control over form submissions on static websites. Built with FastAPI and SMTP, Form2Mail processes form data directly on your server, ensuring maximum security without ongoing costs or limitations. It’s especially suited for developers seeking to maintain control over their data while eliminating reliance on third-party servers.

As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML models…while making it easy to swap in other ML models.

Link to GitHub -->

there are a few tools you can use as "cheat mode" shortcuts to give you a leg up as you're getting started. here's one: https://github.com/bentoml/BentoML

Then you can look into bentoml https://github.com/bentoml/BentoML which is used to deploy ml stuff with many more benifits.

For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.

In this category I’m a big fan of https://github.com/bentoml/BentoML

What I like about it is their idiomatic developer experience. It reminds me of other Pythonic frameworks like Flask and Django in a good way.

I have no affiliation with them whatsoever, just an admirer.

Github Page: https://github.com/bentoml/BentoML