Mastering AI/ML with Docker with 5 Real World Projects

Welcome to the ultimate project-based course on Docker for AI/ML Engineers.Whether you're a machine learning enthusiast, an MLOps practitioner, or a DevOps pro supporting AI teams — this course will teach you how to harness the full power of Docker for AI/ML development, deployment, and consistency.What’s Inside?This course is built around hands-on labs and real projects. You'll learn by doing — containerizing notebooks, serving models with FastAPI, building ML dashboards, deploying multi-service stacks, and even running large language models (LLMs) using Dockerized environments.Each module is a standalone project you can reuse in your job or portfolio.What Makes This Course Different?Project-based learning: Each module has a real-world use case — no fluff.AI/ML Focused: Tailored for the needs of ML practitioners, not generic Docker tutorials.MCP & LLM Ready: Learn how to run LLMs locally with Docker Model Runner and use Docker MCP Toolkit to get started with Model Context ProtocolFastAPI, Streamlit, Compose, DevContainers — all in one course.Projects You'll BuildReproducible Jupyter + Scikit-learn dev environmentFastAPI-wrapped ML model in a Docker containerStreamlit dashboard for real-time ML inferenceLLM runner using Docker Model RunnerFull-stack Compose setup (frontend + model + API)CI/CD pipeline to build and push Docker imagesBy the end of the course, you’ll be able to:Standardize your ML environments across teamsDeploy models with confidence — from laptop to cloudReproduce experiments in one line with DockerSave time debugging “it worked on my machine” issuesBuild a portable and scalable ML development workflow