AI Engineering Full Course: Build & Deploy A Real AI App

Beyond the Demo: What It Takes to Ship an AI App That Actually Works

Anyone can paste a prompt into a chatbot. Building an AI product that runs on its own, handles real emails and survives in production is a different skill entirely. That skill is what this course teaches.

The story of the build

The project is an AI assistant that watches a Gmail inbox and replies to incoming messages. It starts small and grows one layer at a time, so you always understand why each piece exists.

Act 1: Foundations

After a short introduction, you set up the project and get access to the Gmail API. You then write a Gmail client and a poller that checks for new messages, and add a database to keep track of what has been processed.

Act 2: Making it intelligent

The first automated reply is a real milestone. From there you add a knowledge base so answers are grounded in actual information, then build the full AI pipeline that decides how each email should be handled.

Act 3: Making it trustworthy

Here is the part most tutorials skip. You write evals to measure how good the AI’s answers are, so you can improve the system with evidence instead of guesswork.

Act 4: Going live

You set up a server, deploy the whole app with Docker, and build an admin dashboard to monitor and manage everything. The course closes with a wrap-up of what you built and where to go next.

Who is this for?

Developers, students and aspiring AI engineers who know some programming and want a portfolio-worthy project that goes from idea to deployment.

Follow along, build it yourself, and finish with a real AI app running in production. 🚀