Docker for AI/ML
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Docker for AI/ML
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What you'll learn
How to integrate Docker into your AI/ML workflows for better management and deployment.
Set up ML environments and run machine learning applications within Docker containers.
Learn how to containerize AI/ML applications and deploy them to platforms like Hugging Face.
Gain experience using Docker Compose to simulate production-grade ML systems with multiple services.
Skills you'll gain
Details to know
March 2026
7 assignments
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There are 6 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a deep understanding of how Docker integrates with Machine Learning (ML) and Artificial Intelligence (AI). Docker is a powerful tool that streamlines the deployment and management of ML/AI applications, making it a crucial technology for efficient workflows. You will start by learning the importance of Docker in the context of ML and AI, then move on to setting up Docker on your system, configuring tools, and diving into hands-on projects. The course is structured around practical scenarios, such as building a development environment for MLFlow and Jupyter, containerizing ML applications, and simulating production-grade ML systems using Docker Compose. Each section builds on the previous, ensuring a comprehensive understanding of Docker's role in AI/ML workflows. The course progresses with focused topics, including integrating Large Language Models (LLMs) and using Docker Model Runner for local deployment. This course is perfect for developers, data scientists, and AI/ML practitioners who wish to enhance their ability to deploy and manage machine learning systems using Docker. It is suitable for those with a basic understanding of Docker, AI/ML principles, and software development, as the course focuses on hands-on experience. The difficulty level is intermediate. By the end of the course, you will be able to set up ML/AI environments with Docker, containerize applications, simulate production-grade ML systems, and deploy and manage AI models in Docker containers.
In this module, we will explore the importance of Docker in the context of machine learning and artificial intelligence. You will learn how Docker facilitates efficient management and deployment of AI/ML systems. Additionally, you will gain hands-on experience with installing Docker Desktop and setting up your environment for the course.
What's included
5 videos1 reading
5 videos•Total 34 minutes
- Why and How Docker is important for Machine Learning / Artificial Intelligence•9 minutes
- Why and How Docker is important for Machine Learning / Artificial Intelligence (Revisit)•7 minutes
- Docker in the world of LLMs and Agentic AI•5 minutes
- Installing and validating Docker Desktop•8 minutes
- Setting up tools and environment for this course•5 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
In this module, we will dive deep into launching and managing machine learning development environments using Docker. You will gain hands-on experience with Docker concepts and container operations, as well as learn how to set up and integrate MLFlow and Jupyter for seamless experiment tracking and model development.
What's included
7 videos1 assignment
7 videos•Total 66 minutes
- Project - Setup ML Dev Environment with Docker (MLFlow and Jupyter)•3 minutes
- Docker Concepts - Images, Container, Registry, Repository. Pulling Images•12 minutes
- Launch, Analyse, and Connect to MLFlow Container•9 minutes
- Container Operations - Common options, Detaching, Listing, Managing Containers•11 minutes
- Launch JupyterLabs Notebook Environment with a Volume shared with Host•9 minutes
- Writing and executing a simple ML Project with Container Hosted Jupyter Notebook•12 minutes
- Connect Notebook with MLFlow Container for Experiment Tracking•10 minutes
1 assignment•Total 15 minutes
- Launch and Operate ML Dev Environments with Docker - Assessment•15 minutes
In this module, we will focus on packaging machine learning applications as Docker containers. You will learn how to create and test Docker images, build applications using Dockerfiles, and deploy your containerized ML app to platforms like Hugging Face for sharing and collaboration.
What's included
8 videos1 assignment
8 videos•Total 77 minutes
- Project Nebula - Containerize Tech Stack Advisor ML App and Host it on Hugging Face•4 minutes
- Build and test the ML Project and Train the Model•10 minutes
- Why and how to build Container Images Manually First?•6 minutes
- Building a container image step by step using the Imperative Approach•13 minutes
- Building and Testing the Image using Dockerfile•8 minutes
- Analyzing, Tagging and Publishing Container Images•9 minutes
- How to write Dockerfile? Instructions Quick Dive•13 minutes
- Deploy and Host Containerized App to Hugging Face Spaces•12 minutes
1 assignment•Total 15 minutes
- Packaging ML Apps as Container Images with Dockerfiles - Assessment•15 minutes
In this module, we will simulate production-grade machine learning systems using Docker Compose. You will learn how to build and deploy apps, automate ML workflows, and manage multiple services, all while ensuring smooth interaction between various containers in a development environment.
What's included
6 videos1 assignment
6 videos•Total 69 minutes
- Project - Build and Deploy House Price Predictions ML App in Dev with Docker Compose•5 minutes
- Understanding the Application Stack and the ML Workflow•10 minutes
- Automate MLFlow Launch with Code by writing Compose Spec, Learn Compose Syntax•13 minutes
- Run the Data Processing, Feature Engineering and Model Training Pipeline for House Price Prediction•11 minutes
- Composing FastAPI and Streamlit Apps with Multi Service Compose Spec•17 minutes
- Connecting Services using DNS-Based Service Discovery offered by Docker Compose•13 minutes
1 assignment•Total 15 minutes
- Simulating Production Grade ML Systems in Dev with Docker Compose - Assessment•15 minutes
In this module, we will focus on running Large Language Models locally using Docker Model Runner. You will gain practical experience by setting up and launching LLMs in a Docker container, as well as learning how to configure these systems for compatibility with OpenAI APIs and similar platforms.
What's included
5 videos1 assignment
5 videos•Total 47 minutes
- Project - Integrate LocalGPT App with Locally Running LLM using Docker Model Runner•3 minutes
- What is Docker Model Runner? How to Set it up with Docker Desktop?•6 minutes
- Exploring Docker Model Runner - Pull a LLM Model from Gen AI Catalogue and Run it•14 minutes
- Launching LocalGPT App with Docker Model Runner with OpenAI Compatible Connection•10 minutes
- Configuring Docker Model Runner as a Provider to Compose•15 minutes
1 assignment•Total 15 minutes
- Running LLMs Locally with Docker Model Runner - Assessment•15 minutes
In this module, we will explore the Docker MCP Toolkit and its integration with Agentic AI. You will learn the fundamentals of Model Context Protocol, work on hands-on projects, and learn how to manage and automate code revisions while securely connecting with GitHub for collaboration.
What's included
6 videos3 assignments
6 videos•Total 51 minutes
- Project - Explore Docker MCP Toolkit•2 minutes
- What is Model Context Protocol (MCP) and how it sets the foundation for Agentic AI•12 minutes
- Getting started with Docker MCP Toolkit and MCP Catalogue with Gordon AI•11 minutes
- Using Filesystem MCP Server•7 minutes
- Securely connecting to GitHub MCP Server and using Read-Only Access•11 minutes
- Auto-generating Code, Revision Controlling it, and Pushing it to GitHub with MCP•9 minutes
3 assignments•Total 90 minutes
- Exploring Model Context Protocol with Docker MCP Toolkit - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
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Frequently asked questions
Docker is a tool that enables the creation, deployment, and management of applications within containers. For AI and Machine Learning (ML) projects, Docker simplifies the process of packaging code, dependencies, and environments into a consistent and portable format, ensuring that machine learning models and applications run smoothly across various systems. It is particularly relevant because it facilitates efficient deployment, management, and scaling of AI/ML workflows, making it easier for developers to test and deploy their models.
This course, Docker for AI/ML, is designed to teach how to use Docker in the context of machine learning and artificial intelligence applications. It covers topics such as setting up ML development environments, containerizing machine learning applications, simulating production-grade systems with Docker Compose, running large language models (LLMs) locally, and utilizing the Docker MCP Toolkit for Agentic AI. By the end of the course, participants will have practical skills to integrate Docker into their AI/ML workflows and enhance their deployment pipelines.
After completing the course, you'll be able to set up and operate ML environments using Docker, manage containers, containerize machine learning applications, and deploy them on platforms like Hugging Face. You'll also be able to integrate Docker into workflows involving tools like MLFlow, Jupyter, and Docker Compose, and leverage Docker Model Runner to deploy LLMs locally. Additionally, you'll understand the Model Context Protocol (MCP) and how it plays a role in Agentic AI systems.
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