Master Docker for real-world AI & ML workflows — Dockerfiles, Compose, Docker Model Runner, Model Context Protocol (MCP)
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?
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Project-based learning: Each module has a real-world use case — no fluff.
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AI/ML Focused: Tailored for the needs of ML practitioners, not generic Docker tutorials.
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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
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FastAPI, Streamlit, Compose, DevContainers — all in one course.
Projects You’ll Build
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Reproducible Jupyter + Scikit-learn dev environment
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FastAPI-wrapped ML model in a Docker container
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Streamlit dashboard for real-time ML inference
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LLM runner using Docker Model Runner
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Full-stack Compose setup (frontend + model + API)
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CI/CD pipeline to build and push Docker images
By the end of the course, you’ll be able to:
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Standardize your ML environments across teams
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Deploy models with confidence — from laptop to cloud
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Reproduce experiments in one line with Docker
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Save time debugging “it worked on my machine” issues
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Build a portable and scalable ML development workflow
Total Students | 4019 |
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Original Price($) | |
Sale Price | Free |
Number of lectures | 45 |
Number of quizzes | 0 |
Total Reviews | 4 |
Global Rating | 4.875 |
Instructor Name | Gourav Shah . 170,000+ Students |
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