Overview

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This course features Coursera Coach! 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.

Syllabus

Introduction

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.

Launch and Operate ML Dev Environments with Docker

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.

Packaging ML Apps as Container Images with Dockerfiles

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.

Simulating Production Grade ML Systems in Dev with Docker Compose

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.

Running LLMs Locally with Docker Model Runner

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.

Exploring Model Context Protocol with Docker MCP Toolkit

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.