Overview

This tutorial provides a hands-on introduction to Docker containerization for AI/ML.

Topics

  1. Docker basics: Images, containers, Dockerfiles, and key concepts

  2. Building containers: From simple scripts to web applications

  3. Development containers: Creating portable, shareable ML development environments

  4. Publishing images: Sharing your work via Docker Hub

  5. Portability: Verifying your environment works anywhere (GitHub Codespaces)

Why containerization for ML?

In production machine learning systems, containerization simplifies building reliable, scalable, and reproducible pipelines.

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graph TB
    subgraph "Production ML System"
        A[Data Ingestion Container] --> B[Data Cleaning Container]
        B --> C[Feature Store Container]
        C --> D[Training Container]
        D --> E[Model Registry Container]
        E --> F[Model Serving API Container]
        F --> G[API Gateway Container]

        H[(Data Lake)] -.-> A
        I[(Database)] -.-> C
        E -.-> J[Monitoring & Logging Container]
        F -.-> J
    end

    K[Client Applications] --> G
    G --> F

Key benefits:

  • Reproducibility: Exact dependencies across all environments

  • Modularity: Independent scaling and updates of components

  • Portability: Run anywhere without “works on my machine” problems

  • Isolation: Multiple models with conflicting dependencies side-by-side

  • Collaboration: Share entire development environments via images

What you’ll build

Through three hands-on labs (~1 hour total), you’ll:

  1. Data Cleaner: A containerized pandas-based data processing component demonstrating modular ML pipeline components

  2. Streamlit Dashboard: An interactive web app showing port mapping and container networking

  3. ML Dev Container: Your own customized, publishable development environment for course projects

Prerequisites

Before starting, ensure you have:

  • Docker installed (Get Docker)

  • Docker Hub account (Sign up)

  • VS Code with Remote-Containers extension (optional but recommended)

  • GitHub account (for Codespaces verification)

  • Basic Python and command line knowledge

Ready to start?

Head to Docker concepts to understand the fundamentals, or jump straight to Lab 1: Data cleaner container if you’re already familiar with Docker basics.