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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.
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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