Fast, documented Machine Learning APIs with FastAPI
Fast, documented Machine Learning APIs with FastAPI
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 40m | 757 MB
Instructors: Alfredo Deza, Noah Gift
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 40m | 757 MB
Instructors: Alfredo Deza, Noah Gift
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:
* Demo Github Repository with sample code
* Practical MLOps book
* FastAPI Intro tutorial
* RoBERTa ONNX Model for sentiment analysis
Fast, documented Machine Learning APIs with FastAPI
