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Book Overview & Buying
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Table Of Contents
Docker for AI/ML
By :
Docker for AI/ML
By:
Overview of this book
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.
Table of Contents (6 chapters)
Introduction
Launch and Operate ML Dev Environments with Docker
Packaging ML Apps as Container Images with Dockerfiles
Simulating Production Grade ML Systems in Dev with Docker Compose
Running LLMs Locally with Docker Model Runner
Exploring Model Context Protocol with Docker MCP Toolkit