Ashmi Banerjee

Build your backend using FastAPI

Stop wrestling with boilerplate code. Learn how FastAPI's automatic docs and data validation let you build high-performance Python backends faster.

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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