Fast, Documented Machine Learning APIs with FastAPI

Learn how to quickly put together an API that validates requests, and self-documents its endpoints using OpenAPI via Swagger. Quickly produce a robust interface for others to consume your Machine Learning model by following core best-practices of MLOps.
Parts of this video cover the basics of packaging Machine Learning models, as covered in the Practical MLOps book.
Topics include:
* Create a Python project to serve live predictions using FastAPI
* Use a Dockerfile to package the model and the API using Docker containerization
* With minimal Python code, expose an ONNX model to perform sentiment analysis over an HTTP endpoint
* Dynamically interact with the API using the self-documented endpoint in the container.
Useful links:
*

https://github.com/alfredodeza/fastapi-onnx

*

https://learning.oreilly.com/library/view/practical-mlops/9781098103002/

*

https://fastapi.tiangolo.com/tutorial/

*

https://github.com/onnx/models/tree/master/text/machine_comprehension/roberta