Docker for AI/ML: Master Docker for AI/ML - Revolutionizing Machine Learning Environments with Containers

Shah

$64.99

Video Sep 2025 5hrs 44mins 1st Edition

Shah

$64.99

Video Sep 2025 5hrs 44mins 1st Edition

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Key benefits

Docker-based learning for AI/ML workflows, ensuring scalability and reproducibility
Integration of Docker with ML tools like MLFlow, Jupyter, and FastAPI for seamless deployment
Real-world projects to simulate production-grade systems and enhance practical experience

Description

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.

Who is this book for?

This course is designed for Data Scientists and ML Engineers who want to productionize their workflows and streamline development processes. It is also ideal for AI/ML Practitioners seeking to containerize and deploy models with ease. DevOps Engineers supporting AI teams and aiming to build ML-ready pipelines will benefit greatly, as well as AI Hobbyists and Learners interested in running LLMs or dashboards locally using containers. Anyone frustrated by "it worked on my machine" issues in ML environments will find valuable solutions here.