Free Coupon [New] Ultimate Docker Bootcamp for AI/ML,MLOps Practitioners [100% OFF]

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

Free Coupon [New] Ultimate Docker Bootcamp for AI/ML,MLOps Practitioners [100% OFF]

Take advantage of a 100% OFF coupon code for the '[New] Ultimate Docker Bootcamp for AI/ML,MLOps Practitioners' course, created by Gourav Shah . 170,000 Students , School of Devops, available on Udemy.

This course, updated on June 25, 2025 and will be expired on 2025/06/29

This course provides 5 hour(s) 30 minute(s) of expert-led training in English , designed to boost your Other IT & Software skills.

Highly rated at 4.8-star stars from 4 reviews, it has already helped 4,008 students.

This exclusive coupon is shared by Anonymous, at the price 44.99 $ 0 $

Don’t miss this opportunity to level up your skills!

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