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Course Outline

Introduction to Containerization for AI & ML

  • Fundamental concepts of containerization
  • The advantages of using containers for ML workloads
  • Key distinctions between containers and virtual machines

Working with Docker Images and Containers

  • Comprehending images, layers, and registries
  • Managing containers for ML experimentation
  • Utilizing the Docker CLI efficiently

Packaging ML Environments

  • Preparing ML codebases for containerization
  • Managing Python environments and dependencies
  • Integrating CUDA and GPU support

Building Dockerfiles for Machine Learning

  • Structuring Dockerfiles for ML projects
  • Best practices for performance and maintainability
  • Leveraging multi-stage builds

Containerizing ML Models and Pipelines

  • Packaging trained models into containers
  • Managing data and storage strategies
  • Deploying reproducible end-to-end workflows

Running Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services with Docker Compose
  • Monitoring runtime behavior

Security and Compliance Considerations

  • Ensuring secure container configurations
  • Managing access and credentials
  • Handling confidential ML assets

Deploying to Production Environments

  • Publishing images to container registries
  • Deploying containers in on-prem or cloud setups
  • Versioning and updating production services

Summary and Next Steps

Requirements

  • A foundational understanding of machine learning workflows
  • Experience with Python or comparable programming languages
  • Familiarity with basic Linux command-line operations

Target Audience

  • ML engineers responsible for deploying models to production
  • Data scientists overseeing reproducible experiment environments
  • AI developers constructing scalable containerized applications
 14 Hours

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