Ashmi Banerjee
Build your backend using FastAPI
#1about 2 minutes
Prerequisites and agenda for the FastAPI talk
The talk requires a basic understanding of Python and web development concepts to cover web architecture, framework comparisons, and a FastAPI implementation.
#2about 2 minutes
Understanding modern versus traditional web architecture
Web architecture has shifted from monolithic servers rendering all HTML to a modern approach using a CDN for static assets and APIs for dynamic JSON data.
#3about 6 minutes
Comparing Django, Flask, and FastAPI frameworks
Django is a full-stack framework, Flask is a lightweight alternative, and FastAPI excels at building modern APIs with less boilerplate code.
#4about 1 minute
Exploring the key features and benefits of FastAPI
FastAPI offers automatic interactive documentation via Swagger UI, asynchronous operations for performance, and built-in data validation to simplify development.
#5about 5 minutes
Implementing an ML image classifier with FastAPI
Learn how to serve a machine learning model by creating GET and POST endpoints in FastAPI to handle image URL input and return predictions as JSON.
#6about 2 minutes
Running the server and demonstrating the API
The application is run using Uvicorn, and a live demo shows how to interact with the API endpoints directly in the browser using the auto-generated documentation.
#7about 1 minute
Preparing your FastAPI application for production
Key steps after development include writing thorough tests, containerizing the application with Docker, and deploying it to a cloud service for accessibility.
#8about 2 minutes
Recap and audience questions on FastAPI
The talk concludes with a summary of FastAPI's benefits and a Q&A session covering comparisons to TensorFlow Serve and the use of async functions.
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