You know Machine Learning, your models are working well, the team likes the results… but now you need to “serve” them in an API so that others can interact with it (developers/frontend team/other systems).
In this talk, you will learn how to easily build a production-ready web (JSON) API for your ML models with FastAPI, including best practices by default... explained with memes.
With very little code, you will get automatic/interactive documentation, data validation, authentication, open standards (OpenAPI, JSON Schema, OAuth2), and the best performance available in Python (on par with Go and NodeJS).
On top of that, you will have autocompletion and type checks in your editor, even for your own data, no matter the complexity of its shape.
The talk is targeted at Machine Learning practitioners that only know the basics of web development: what is an API, HTTP, JSON, etc. But can be appropriate for anyone interested in building web APIs. It’s a very practical talk showing working code examples.