[New] Ultimate Docker Bootcamp for ML, GenAI and Agentic AI | Udemy [Update 07/2025]
English | Size: 2.6 GB
Genre: eLearning[/center]

Master Docker for real-world AI & ML workflows - Dockerfiles, Compose, Docker Model Runner, Model Context Protocol (MCP)

What you'll learn
Run and manage Docker containers tailored for AI/ML workflows
Containerize Jupyter notebooks, Streamlit dashboards, and ML development environments
Package and deploy Machine Learning models with Dockerfile
Publish your ML Projects to Hugging Face Spaces
Push and pull images from DockerHub and manage Docker image lifecycle
Apply Docker best practices for reproducible ML research and collaborative projects
LLM Inference with Docker Model Runner
Setup Agentic AI Workflows with Docker Model Context Protocol (MCP) Toolkit
Build and Deploy Containerised ML Apps with Docker Compose

Welcome to the ultimate project-based course on Docker for AI/ML Engineers.

Whether you're a machine learning enthusiast, an MLOps practitioner, or a DevOps pro supporting AI teams - this course will teach you how to harness the full power of Docker for AI/ML development, deployment, and consistency.

What's Inside?

This course is built around hands-on labs and real projects. You'll learn by doing - containerizing notebooks, serving models with FastAPI, building ML dashboards, deploying multi-service stacks, and even running large language models (LLMs) using Dockerized environments.

Each module is a standalone project you can reuse in your job or portfolio.

What Makes This Course Different?

Project-based learning: Each module has a real-world use case - no fluff.

AI/ML Focused: Tailored for the needs of ML practitioners, not generic Docker tutorials.

MCP & LLM Ready: Learn how to run LLMs locally with Docker Model Runner and use Docker MCP Toolkit to get started with Model Context Protocol

FastAPI, Streamlit, Compose, DevContainers - all in one course.

Projects You'll Build

Reproducible Jupyter + Scikit-learn dev environment

FastAPI-wrapped ML model in a Docker container

Streamlit dashboard for real-time ML inference

LLM runner using Docker Model Runner

Full-stack Compose setup (frontend + model + API)

CI/CD pipeline to build and push Docker images

By the end of the course, you'll be able to:

Standardize your ML environments across teams

Deploy models with confidence - from laptop to cloud

Reproduce experiments in one line with Docker

Save time debugging "it worked on my machine" issues

Build a portable and scalable ML development workflow

Who this course is for:
Data Scientists and ML Engineers who want to productionize their workflows
AI/ML Practitioners looking to containerize and deploy models easily
DevOps Engineers supporting AI teams and looking to build ML-ready pipelines
AI Hobbyists and Learners who want to run LLMs or dashboards locally using containers
Anyone tired of "it works on my machine" issues in ML environments

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