Module 1 · Deployment Foundations

OBJECTIVE: understand how an AI project goes from a notebook to a product people can use — the parts of a real AI application, how they talk to each other over the web, and where and how they are deployed and run. WHAT WE COVER: • From notebook to product: what 'deployment' means and the parts of an AI app • Web basics: client and server, HTTP methods, status codes, JSON, REST APIs • Configuration and secrets: environment variables, .env files, never keys in code • Git and GitHub workflow for a team: branches, pull requests • Terminal and Linux basics you need on a server • Where to deploy: demo hosts, platforms (PaaS), containers on cloud, serverless, Kubernetes — and how to choose • MLOps vs LLMOps: the life cycle after launch AFTER THIS MODULE YOU CAN: • Draw the architecture of an AI app and explain each part • Call and test any API with Python and Postman • Choose the right place to deploy a project PREREQUISITES: basic Python. If you followed the full path, the 'Already covered' column tells you what to skip.