Mastering AI/ML with Docker with 5 Real World Projects

Master Docker for real-world AI & ML workflows — Dockerfiles, Compose, Docker Model Runner, Model Context Protocol (MCP)

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

Requirements
- 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!

Description
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

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
More Info