- Basic understanding of Python — you don’t need to be an expert, but you should be comfortable running scripts or working in notebooks.
- Familiarity with Machine Learning concepts — knowing what a model is, and having used libraries like scikit-learn, pandas, or TensorFlow will help.
- Laptop with Docker/Rancher installed — we’ll walk you through setting up Docker Desktop for Windows, macOS, or Linux.
- A GitHub account (recommended) — for accessing project code and pushing your own.
- Curiosity to build real-world AI/ML projects with Docker — no prior Docker experience is required!
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.
- 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
- 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
- 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
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