What you'll learn:
- Run and manage Docker containers tailored for AI/ML workflows
- Containerize Jupyter notebooks, Streamlit dashboards, and ML development environments
- Package and deploy Machine Learning models with Dockerfile
- Publish your ML Projects to Hugging Face Spaces
- Push and pull images from DockerHub and manage Docker image lifecycle
- Apply Docker best practices for reproducible ML research and collaborative projects
- LLM Inference with Docker Model Runner
- Setup Agentic AI Workflows with Docker Model Context Protocol (MCP) Toolkit
- Build and Deploy Containerised ML Apps with Docker Compose
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 Dockerfor AI/ML development, deployment, and consistency.
What’s Inside?
This course is built aroundhands-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
