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Fast, Documented Machine Learning APIs with FastAPI

 


Fast, Documented Machine Learning APIs with FastAPI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 40m | Size: 757.8 MB
Use FastAPI to expose an HTTP API for fast live predictions using an ONNX Machine Learning Model. FastAPI is a Python web framework that provides easy development of documented HTTP APIs by offering self-documented endpoints with Swagger - a tool to describe, document, and use RESTful web services.


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


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