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[New] Ultimate Docker Bootcamp for ML, GenAI and Agentic AI

January 19, 2026 Udemy

FREE ~~$19.99~~ GET THIS DEAL

JAN_FREE_02

Description

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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