Apr 26
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In this video, we begin building a production-style AI assistant API using FastAPI. Most AI tutorials stop at simple demos, but in this project we are building something closer to what real engineering teams would deploy. The goal is to create an AI assistant service that accepts messages through an API, sends those messages to a language model, and returns the generated response. This first video focuses on building the core application layer. We will: • create the FastAPI project • structure the application • add a health endpoint • build the AI chat endpoint • connect the API to a language model • test the assistant locally Once the core application is working, we will begin productionizing the system in the next videos. Later in the series we will: • containerize the service using Docker • automate builds with GitHub Actions • provision Azure infrastructure using Terraform • push images to Azure Container Registry • deploy the AI API to the cloud This project is designed to show how AI services are actually built, packaged, and deployed in modern DevOps workflows. Project Architecture (Phase 1) User ↓ FastAPI API ↓ LLM Provider ↓ Response returned to user Later the architecture will expand to include CI/CD, container registries, and cloud infrastructure. Join the DevOps & AI Community If you want the full project templates and more real-world DevOps and AI infrastructure builds, join the community: Skool Community 👉https://www.skool.com/nimbus-compute-academy-8317/about Discord Community 👉 https://discord.gg/52HMnXxaE9 Tools Used FastAPI Python OpenAI API Uvicorn Docker (coming next) Next Video in the Series In the next video we will containerize this AI assistant using Docker, which allows the application to run consistently across environments and prepares it for CI/CD pipelines.