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Fast, documented Machine Learning APIs with FastAPI /
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....
| Main Authors: | , |
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| Corporate Author: | |
| Format: | Video |
| Language: | Inglés |
| Published: |
[Erscheinungsort nicht ermittelbar] :
Pragmatic AI Solutions,
2021
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| Edition: | 1st edition. |
| Online Access: | Texto completo (Requiere registro previo con correo institucional) |
| Summary: | 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 which 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. |
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| Item Description: | Online resource; Title from title screen (viewed July 16, 2021). |
| Physical Description: | 1 online resource (1 video file, circa 40 min.) |