Your AI Project Works Locally… But Why? | Docker for AI Engineers

Your AI project works perfectly on your laptop… but suddenly breaks when you deploy it? Different Python versions, dependency conflicts, missing packages, environment mismatches — these problems are extremely common when moving AI applications from development to production. In this video, we’ll understand Docker specifically from an AI Engineer’s point of view. We’ll cover: • What “Works on My Machine” actually means • Why AI projects break in different environments • What Docker is and how containers work • Docker vs Virtual Machines • Dockerfile, Image, and Container • How to package an AI project with Docker • Dockerizing a FastAPI / AI application • Essential Docker commands • Docker Compose for multiple AI services • GPU-based AI workloads with Docker • How Docker helps move AI applications toward production By the end, you’ll have a clear mental model of: Dockerfile → Image → Container → AI Application → Production If you’re learning AI/ML Engineering, Generative AI, RAG, FastAPI, or LLM application development, Docker is an important production skill to understand. #Docker #AIEngineering #MachineLearning #GenerativeAI #Python #FastAPI #MLOps #AIEngineer #RAG #DevOps