Build A Production-Ready Fastapi Ai App

Build A Production-Ready Fastapi Ai App
Published 8/2026
Created by Rahul Mula
MP4 | Video: h264, 2560x1440 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 83 Lectures ( 5h 51m ) | Size: 4.4 GB
Master FastAPI to build an AI App. Integrate background AI workers, usage-based pricing, and deploy live to the web
What you'll learn
Use agentic development to build a production-ready FastAPI app from scratch and deploy it live on Render
Learn backend fundamentals with FastAPI like request-response cycle, HTTP methods, and Pydantic data validations
Store data using a PostgreSQL database, SQLModel, handle SQL relationships and perform database migrations with Alembic
Implement secure JWT user authentication and integrate a third-party authentication service to your backend API
Run OpenAI's Whisper model in Celery background workers
Store and process audio files in the cloud using Cloudflare R2 Object Storage and Replicate
Monetize your application with usage-based pricing with Polar
Generate a lightweight React UI for your FastAPI App with Copilot and Shadcn UI
Requirements
Python
Description
In this course, you'll masterFastAPI by building an AI Audio Censoring application from scratch.
The curriculum is designed to teach you the big picture of building a production-ready app, combined with modernAgentic Development. The goal isn't to force you to memorize the exact code; it's to give you thearchitectural fundamentals so you can apply these skills to any future project.
We'll start with a solid introduction to FastAPI, covering things like the request-response cycle, HTTP methods, and data validation. We start with visual animations to break down core backend concepts before diving into the code.
You won't just learn basic APIs-you will build a production-ready system. You'll learn how to
Build async APIs.
JWT authentication.
Handle SQL databases.
Run AI models like OpenAI's Whisper in background workers.
Scale with Cloud Object Storage and Replicate.
Monetize your app with usage-based pricing using Polar.
Implement third-party auth.
API security, observability and testing.
To tie it all together, we'll connect a lightweight React frontend so you can see your API in action. Finally, I'll walk you step-by-step through deploying the entire stack-database, workers, server, and UI-live to the web.
By the time you finish this course, you'll have a fully functional application, deployed live, ready for real users.
Who this course is for
Python developers ready to build, monetize, and deploy an AI app.
Homepage

You do not have permission to view the full content of this post. Log in or register now.

You do not have permission to view the full content of this post. Log in or register now.