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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)
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
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
