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[100% Off] [New] Ultimate Docker Bootcamp For Ml, Genai And Agentic Ai

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

Added on September 3, 2025 IT & Software 2 min read

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

Requirements

• Basic understanding of Python — you don’t need to be an expert
• but you should be comfortable running scripts or working in notebooks.
• Familiarity with Machine Learning concepts — knowing what a model is
• and having used libraries like scikit-learn
• pandas
• or TensorFlow will help.
• Laptop with Docker/Rancher installed — we’ll walk you through setting up Docker Desktop for Windows
• macOS
• or Linux.
• A GitHub account (recommended) — for accessing project code and pushing your own.
• Curiosity to build real-world AI/ML projects with Docker — no prior Docker experience is required!

Description

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