Module 13 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
The last mile. Shipping it like an engineer instead of leaving it in a notebook. Calling LLM APIs in production the right way, with auth, rate limits, streaming, error handling, and retries. Then the architecture around them: serverless and edge functions, queues, webhooks, somewhere to store the data. Plus the operational stuff that keeps you out of trouble. Secrets handled properly, cost controls, and enough observability to see what the system is doing and what it’s costing you.
Use whatever stack you already run as the canvas. Extending something live teaches more than starting from a blank page.
Why it matters
“You own everything. Code in your repo, data in your database.” That promise only holds if you can actually deploy and run these systems. This module is what makes the ownership pitch true instead of a thing you say. It’s also the difference between a consultant who hands over a prototype and one who hands over infrastructure.
Build this
One AI feature shipped to production, with logging and a cost dashboard, running on infrastructure you control. Proof you can operate the thing, not just prototype it.
How to work through it
- The Anthropic and OpenAI API docs and cookbooks, the production patterns
- The docs for your hosting platform’s serverless functions
- The deployment chapters of Chip Huyen’s AI Engineering
- Ship one feature with logging and a cost dashboard
Resources
- Anthropic and OpenAI API docs and cookbooks
- Netlify’s functions docs
- AI Engineering by Chip Huyen, the deployment chapters