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Docker and Containerization for ML Projects

Chapter 1: Docker Concepts for Machine Learning

Why Containerize ML Projects?

Docker Image Fundamentals

Container Lifecycle Management

Introduction to Dockerfiles

Docker Registries and Repositories

Hands-on practical: Running Pre-built ML Images

Quiz for Chapter 1

Chapter 2: Building Custom ML Environments with Dockerfiles

Structuring your Dockerfile

Choosing the Right Base Image

Managing Python Dependencies (pip)

Managing Python Dependencies (Conda)

Working with Environment Variables

Copying Code and Artifacts

Setting the Working Directory and Entrypoint

Hands-on practical: Build a Scikit-learn Environment

Quiz for Chapter 2

Chapter 3: Managing Data and Models in Containers

Understanding Container Storage

Using Bind Mounts for Development

Using Docker Volumes for Persistence

Comparing Bind Mounts and Volumes

Accessing Cloud Storage from Containers

Packaging Models Inside Images vs. Volumes

Hands-on practical: Mounting Datasets and Saving Models

Quiz for Chapter 3

Chapter 4: Containerizing ML Training Workflows

Structuring Training Scripts for Containers

Passing Configuration and Hyperparameters

Running Training Jobs with docker run

Managing Training Logs

GPU Acceleration for Training

Introduction to Docker Compose for Training Stacks

Hands-on practical: Containerize and Run a Training Script

Quiz for Chapter 4

Chapter 5: Containerizing ML Models for Inference

Designing Inference Services

Building Inference APIs (Flask/FastAPI)

Optimizing Image Size: Multi-Stage Builds

Reducing Dependencies for Inference

Exposing Ports for API Access

Health Checks for Inference Containers

Hands-on practical: Containerize a Simple Inference API

Quiz for Chapter 5

Chapter 6: Streamlining Workflows with Docker Compose

Introduction to Docker Compose

Defining Services in docker-compose.yml

Networking Between Containers

Using Volumes with Compose

Environment Variables in Compose

Building Images with Compose

Common ML Stack Examples (e.g., API + DB)

Hands-on practical: Develop an ML App with Compose

Quiz for Chapter 6

Building Images with Compose

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References

Compose file build reference, Docker, 2024 (Docker) - Official documentation detailing the build keyword in Docker Compose, including context, Dockerfile specification, build arguments, and combining with the image directive. This resource is essential for understanding how to build custom images directly within your Compose setup.
Dockerfile reference, Docker, 2024 (Docker) - The official guide to Dockerfile syntax and instructions. While the section focuses on Compose, the build keyword relies on Dockerfiles for image creation, making this a foundational reference for understanding the underlying build process.
Overview of Docker Compose, Docker, 2024 (Docker) - Provides a high-level introduction to Docker Compose, explaining its purpose for defining and running multi-container Docker applications. It establishes the foundational understanding of Compose services, which the build feature enhances.
Docker: Up & Running: Building, Scaling, and Structuring Applications with Containers, Karl Matthias, Sean P. Kane, 2022 (O'Reilly Media) - A comprehensive book that covers Docker fundamentals, including image building and multi-container application management with Docker Compose. Chapters on Compose provide broader context for defining services and integrating custom builds.

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