Docker for AI/ML

Hands-on introduction to containerization for machine learning and data science.

What you’ll build: Through three hands-on labs (~1 hour), you’ll containerize data processing scripts, web applications, and finally create your own publishable ML development environment.

Note

Start with the Overview for an introduction to containerization.

Outline

• Overview
  • Topics
  • Why containerization for ML?
  • What you’ll build
  • Prerequisites
  • Ready to start?
• Docker concepts
  • Core terminology
  • Common commands
  • Why Docker for ML?
  • Next steps
• Dockerfile guide
  • Essential instructions
  • Complete example
  • Best practices
  • Common patterns
  • Debugging tips
  • Next steps
• Lab 1: Data cleaner container
  • Learning objectives
  • What’s included
  • The scenario
  • Step-by-step instructions
  • Key concepts
  • Experiment further
  • What’s next?
• Lab 2: Streamlit dashboard container
  • Learning objectives
  • What’s included
  • The scenario
  • Step-by-step instructions
  • Key concepts
  • Use cases for ML/data science
  • Experiment further
  • What’s next?
• Lab 3: ML development container
  • Learning objectives
  • What’s included
  • The scenario
  • Part 1: Build and test your development image
  • Part 2: Customize your environment
  • Part 3: Publish to Docker Hub
  • Part 4: Use your image as a VS Code dev container
  • Part 5: Verify portability with GitHub Codespaces
  • Key concepts
  • Real-world ML applications
  • Alternative registries
  • Experiment further
  • Troubleshooting
  • What you’ve accomplished
  • Next steps
  • Congratulations!