Master Docker for real-world AI & ML workflows — Dockerfiles, Compose, Docker Model Runner, Model Context Protocol (MCP)
Note: Course Free for limited time and limited users(500 limit) so Enroll ASAP. If not free it means you are late. Scroll Down and click Enroll Now button to get enrolled
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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
Explore related topics
This course includes:
- Role Play
- 5.5 hours on-demand video
- 8 articles
- 3 downloadable resources
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
Course content
6 sections • 45 lectures • 6h 4m total length
- Why and How Docker is important for Machine Learning / Artificial Intelligence
- Why and How Docker is important for Machine Learning / Artificial Intelligence
- Docker in the world of LLMs and Agentic AI
- Download the Slides Deck
- Installing and validating Docker Desktop
- Setting up tools and environment for this Course
- Explaining Docker to a Skeptical Stakeholder
- Quick Quiz: Why Docker Matters for AI/ML
- Project – Setup ML Dev Environment with Docker – MLFlow and Jupyter
- Docker Concepts – Images, Container, Registry, Repository. Pulling Images
- Launch, Analyse and Connect to MLFlow Container
- Container Operations – Common options, Detaching, Listing, Managing Containers
- Launch JupyterLabs Notebook Envioronment with a Volume shared with Host
- Writing and executing a simple ML Project with Container Hosted Jupyter Notebook
- Connect Notebook with MLFlow Container for Experiment Tracking
- Project Guide -Build it Yourself
- Lab Review: Running MLflow & Jupyter with Docker
- Project Nebula – Containerize Tech Stack Advisor ML App and Host it on Hugging F
- Mission Statement
- Build and test the ML Project and Train the Model
- Why and how to build Container Images Manually First ?
- Building a container image step by step using the Imperative Approach
- Buidling and Testing the Image using Dockerfile
- Analysing, Tagging and Publishing Container Images
- How to write Dockerfile ? Instructions Quick Dive
- Deploy and Host Containerized App to Hugging Face Spaces
- Project Guide -Build it Yourself
- Quick Quiz: Containerizing ML Apps with Dockerfile
- Building a Local ML Stack with Docker Compose
- Project – Build and Deploy House Price Predictions ML App in Dev with Docker Co
- Mission Statement
- Understanding the Application Stack and the ML Workflow
- Automate MLFlow Launch with Code by writing Compose Spec , Learn Compose Syntax
- Run the Data Processing, Feature Engineering and Model Training Pipeline for Hou
- Composing FastAPI and Streamlit Apps with Multi Service Compose Spec
- Connecting Services using DNS Based Service Discovery offered by Docker Compose
- Project Guide – Build it Yourself
- Project – Integrate LocalGPT App with Locally Running LLM using Docker Model Run
- Mission Statement
- What is Docker Model Runner ? How to Set it up with Docker Desktop ?
- Exploring Docker Model Runner – Pull a LLM Model from Gen AI Catalogue and Run i
- Lanching LocalGPT App with Docker Model Runner with OpenAI Compatible Connection
- Configuring Docker Model Runner as a Provider to Compose
- Quick Quiz: Running Local LLMs with Docker Model Runner
- Project – Explore Docker MCP Toolkit
- Mission Statement
- What is Model Context Protocol (MCP) and how it sets the foundation for Agentic
- Getting started with Docker MCP Toolkit and MCP Catalogue with Gordon AI
- Using Filesystem MCP Server
- Securely connecting to GitHub MCP Server and using Read Only Access
- Auto generating Code, Revision Controlling it and Pushing it toGitHub with MCP S
- Quick Quiz: Connecting LLMs to Tools with Docker MCP
- 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 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
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
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