Build a Production-Ready FastAPI AI AppsPublished 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
⚡ 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
DescriptionIn this course, you'll master
FastAPI 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 modern
Agentic Development. The goal isn't to force you to memorize the exact code; it's to give you the
architectural 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.
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