Overview
In this project, you'll take a containerized FastAPI application from local development to a publicly accessible cloud deployment. You'll modify your app to be deployment-ready, expose it with ngrok for quick testing, then deploy to PaaS platforms like Render or Railway.
Along the way, you'll configure Dockerfiles to read environment variables, handle database initialization failures gracefully, and manage production configuration separately from local .env files. Each step builds toward a robust, publicly accessible API.
This hands-on experience mirrors real-world deployment workflows and strengthens your ability to ship production-ready AI services — a critical skill for AI engineers working beyond the notebook.
What You'll Learn
- ✓ Modify Dockerfile CMD to read PORT environment variable
- ✓ Handle database initialization failures gracefully without crashing
- ✓ Expose local Docker services publicly using ngrok tunneling
- ✓ Deploy containerized applications to PaaS platforms like Render/Railway
- ✓ Configure production environment variables separately from local .env files
Project Steps
9 steps
- 1 Introduction
- 2 Make the App Deployment-Ready
- 3 Go Public with ngrok
- 4 Deploying to a PaaS
- 5 Deploy with Render
- 6 Deploy with Railway
- 7 Use Your Deployed Service
- 8 Clean Up Your Deployments
- 9 Next Steps
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