Mastering Ai/ml With Docker With 5 Real World Projects
Published 5/2025
Created by Gourav Shah . 160,000+ Students
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 45 Lectures ( 6h 4m ) | Size: 2.65 GB
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 ProtocolFastAPI, Streamlit, Compose, DevContainers - all in one course.Projects You'll BuildReproducible Jupyter + Scikit-learn dev environmentFastAPI-wrapped ML model in a Docker containerStreamlit dashboard for real-time ML inferenceLLM runner using Docker Model RunnerFull-stack Compose setup (frontend + model + API)CI/CD pipeline to build and push Docker imagesBy the end of the course, you'll be able to:Standardize your ML environments across teamsDeploy models with confidence - from laptop to cloudReproduce experiments in one line with DockerSave time debugging "it worked on my machine" issuesBuild a portable and scalable ML development workflow
Who this course is for
Data Scientists and ML Engineers who want to productionize their workflows
AI/ML Practitioners looking to containerize and deploy models easily
DevOps Engineers supporting AI teams and looking to build ML-ready pipelines
AI Hobbyists and Learners who want to run LLMs or dashboards locally using containers
Anyone tired of "it works on my machine" issues in ML environments
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