Docker for MLOps & AIOps – School of Devops

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 FastAPI in Docker
Push and pull images from DockerHub and manage Docker image lifecycle
Apply Docker best practices for reproducible ML research and collaborative projects
Local 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

Course Content

Artificial Intelligence and Machine Learning with Docker – Introduction

Why and How Docker is important for Machine Learning / Artificial Intelligence

08:48
Who is using Docker for AI ? Use cases from Netflix, Uber, Amazon, Nasa

06:34
Docker in the world of LLMs and Agentic AI

05:06
Installing and validating Docker Desktop

08:16
Setting up tools and environment for this Course

05:22

MLOps – Launch and Operate ML Dev Environments with Docker

Project – Setup ML Dev Environment with Docker – MLFlow and Jupyter

02:38
Docker Concepts – Images, Container, Registry, Repository. Pulling Images

11:38
Launch, Analyse and Connect to MLFlow Container

09:07
Container Operations – Common options, Detaching, Listing, Managing Containers

11:17
Launch JupyterLabs Notebook Envioronment with a Volume shared with Host

09:28
Writing and executing a simple ML Project with Container Hosted Jupyter Notebook

12:00
Connect Notebook with MLFlow Container for Experiment Tracking

10:20
Project Lab Guide
Python Basics Quiz

MLOps – Packaging ML Apps as Container Images with Dockerfiles

Project Nebula – Containerize Tech Stack Advisor ML App and Host it on Hugging Face Spaces

04:05
Build and test the ML Project and Train the Model

10:28
Why and how to build Container Images Manually First ?

06:07
Building a container image step by step using the Imperative Approach

13:23
Buidling and Testing the Image using Dockerfile

09:15
Analysing, Tagging and Publishing Container Images

09:15
How to write Dockerfile ? Instructions Quick Dive

13:11
Deploy and Host Containerized App to Hugging Face Spaces

12:24
Project Lab Guide

MLOps – Simualting Production Grade ML Systems in Dev with Docker Compose

Project – Build and Deploy House Price Predictions ML App in Dev with Docker Compose

04:52
Mission Statement
Understanding the Application Stack and the ML Workflow

10:19
Automate MLFlow Launch with Code by writing Compose Spec , Learn Compose Syntax

12:52
Run the Data Processing, Feature Engineering and Model Training Pipeline for House Price

10:55
Composing FastAPI and Streamlit Apps with Multi Service Compose Spec

16:55
Connecting Services using DNS Based Service Discovery offered by Docker Compose

12:49
Project Lab Guide

AIOps – Running LLMs Locally with Docker Model Runner

AIOps – Exploring Model Context Protocol with Docker MCP Toolkit

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What Will You Learn?

Course Content

Artificial Intelligence and Machine Learning with Docker – Introduction

Why and How Docker is important for Machine Learning / Artificial Intelligence

Who is using Docker for AI ? Use cases from Netflix, Uber, Amazon, Nasa

Docker in the world of LLMs and Agentic AI

Installing and validating Docker Desktop

Setting up tools and environment for this Course

MLOps – Launch and Operate ML Dev Environments with Docker

Project – Setup ML Dev Environment with Docker – MLFlow and Jupyter

Docker Concepts – Images, Container, Registry, Repository. Pulling Images

Launch, Analyse and Connect to MLFlow Container

Container Operations – Common options, Detaching, Listing, Managing Containers

Launch JupyterLabs Notebook Envioronment with a Volume shared with Host

Writing and executing a simple ML Project with Container Hosted Jupyter Notebook

Connect Notebook with MLFlow Container for Experiment Tracking

Project Lab Guide

Python Basics Quiz

MLOps – Packaging ML Apps as Container Images with Dockerfiles

Project Nebula – Containerize Tech Stack Advisor ML App and Host it on Hugging Face Spaces

Build and test the ML Project and Train the Model

Why and how to build Container Images Manually First ?

Building a container image step by step using the Imperative Approach

Buidling and Testing the Image using Dockerfile

Analysing, Tagging and Publishing Container Images

How to write Dockerfile ? Instructions Quick Dive

Deploy and Host Containerized App to Hugging Face Spaces

Project Lab Guide

MLOps – Simualting Production Grade ML Systems in Dev with Docker Compose

Project – Build and Deploy House Price Predictions ML App in Dev with Docker Compose

Mission Statement

Understanding the Application Stack and the ML Workflow

Automate MLFlow Launch with Code by writing Compose Spec , Learn Compose Syntax

Run the Data Processing, Feature Engineering and Model Training Pipeline for House Price

Composing FastAPI and Streamlit Apps with Multi Service Compose Spec

Connecting Services using DNS Based Service Discovery offered by Docker Compose

Project Lab Guide

AIOps – Running LLMs Locally with Docker Model Runner

Project – Integrate LocalGPT App with Locally Running LLM using Docker Model Runner

Mission Statement

What is Docker Model Runner ? How to Set it up with Docker Desktop ?

Exploring Docker Model Runner – Pull a LLM Model from Gen AI Catalogue and Run it Locally

Lanching LocalGPT App with Docker Model Runner with OpenAI Compatible Connection String

Configuring Docker Model Runner as a Provider to Compose

AIOps – Exploring Model Context Protocol with Docker MCP Toolkit

Project – Explore Docker MCP Toolkit

Mission Statement

What is Model Context Protocol (MCP) and how it sets the foundation for Agentic AI

Getting started with Docker MCP Toolkit and MCP Catalogue with Gordon AI

Using Filesystem MCP Server

Securely connecting to GitHub MCP Server and using Read Only Access

Auto generating Code, Revision Controlling it and Pushing it toGitHub with MCP Server and LLM

Student Ratings & Reviews

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