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 Duration 14 hours

Course Outline

Foundations of Containerization for AI & ML

  • Fundamental principles of containerization
  • Why containers suit ML workloads
  • Distinctions between containers and virtual machines

Utilizing Docker Images and Containers

  • Exploring images, layers, and registries
  • Overseeing containers for ML experimentation
  • Efficient use of the Docker CLI

Encapsulating ML Environments

  • Readying ML codebases for containerization
  • Handling Python environments and dependencies
  • Incorporating CUDA and GPU capabilities

Creating Dockerfiles for Machine Learning

  • Organizing Dockerfiles for ML projects
  • Best practices for performance and upkeep
  • Leveraging multi-stage builds

Containerizing ML Models and Pipelines

  • Bundling trained models into containers
  • Handling data and storage strategies
  • Implementing reproducible end-to-end workflows

Operationalizing Containerized ML Services

  • Creating API endpoints for model inference
  • Scaling services via Docker Compose
  • Tracking runtime performance

Security and Compliance Perspectives

  • Securing container configurations
  • Controlling access and credentials
  • Protecting sensitive ML assets

Production Deployment

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

Conclusion and Future Directions

Requirements

  • Knowledge of machine learning workflows
  • Proficiency with Python or comparable programming languages
  • Basic command of Linux command-line operations

Target Audience

  • ML engineers responsible for production model deployment
  • Data scientists maintaining reproducible experiment setups
  • AI developers creating scalable containerized applications

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