In this course, you’ll master Docker for AI/ML development, deployment, and maintenance. The course begins with an introduction to Docker’s significance in machine learning and AI, demonstrating how containers provide reproducibility and scalability. You will set up Docker environments to run ML tools, including Jupyter, MLFlow, and more, preparing you for tasks like packaging models and deploying them on production systems. The curriculum is structured around practical, project-based learning. You will progress from setting up basic ML environments to packaging ML applications as containerized images. The course dives into Dockerfiles, container operations, and how to integrate services using Docker Compose, allowing you to simulate production-grade systems. By exploring projects, such as deploying a house price prediction app or running LLMs locally, you’ll manage complex workflows in Docker. You’ll also explore Docker’s Model Context Protocol (MCP) Toolkit, essential for managing AI/ML applications, including agentic AI and LLMs. By the end, you’ll be able to deploy AI models locally, create reproducible environments, and automate pipelines with Docker.