(https://i115.fastpic.ru/big/2021/0717/2c/828d67b6983fe1ef16b893912628ed2c.jpeg)
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.
(https://i115.fastpic.ru/big/2021/0717/43/c47e14c69e06de219e9111aa573a7043.jpeg)
Download link:
Só visivel para registados e com resposta ao tópico.Only visible to registered and with a reply to the topic.Links are Interchangeable - No Password - Single Extraction