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 Protocol

• FastAPI, Streamlit, Compose, DevContainers — all in one course.

Projects You’ll Build

• Reproducible Jupyter + Scikit-learn dev environment

• FastAPI-wrapped ML model in a Docker container

• Streamlit dashboard for real-time ML inference

• LLM runner using Docker Model Runner

• Full-stack Compose setup (frontend + model + API)

• CI/CD pipeline to build and push Docker images

By the end of the course, you’ll be able to:

• Standardize your ML environments across teams

• Deploy models with confidence — from laptop to cloud

• Reproduce experiments in one line with Docker

• Save time debugging “it worked on my machine” issues

• Build a portable and scalable ML development workflow

Who this course is for:

• Data Scientists and ML Engineers who want to productionize their workflows
• AI/ML Practitioners looking to containerize and deploy models easily
• DevOps Engineers supporting AI teams and looking to build ML-ready pipelines
• AI Hobbyists and Learners who want to run LLMs or dashboards locally using containers
• Anyone tired of “it works on my machine” issues in ML environments