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Course Outline

Foundations of Containerization for AI and ML

  • Fundamental concepts of containerization
  • The advantages of containers for ML workloads
  • Distinctions between containers and virtual machines

Managing Docker Images and Containers

  • Concepts of images, layers, and registries
  • Container management for ML experimentation
  • Efficient utilization of the Docker CLI

Packaging ML Environments

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

Creating Dockerfiles for Machine Learning

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

Containerizing ML Models and Pipelines

  • Encapsulating trained models within containers
  • Strategies for data and storage management
  • Implementing reproducible end-to-end workflows

Running Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services via Docker Compose
  • Monitoring runtime performance

Security and Compliance

  • Securing container configurations
  • Managing access controls and credentials
  • Protecting confidential ML assets

Production Deployment

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

Summary and Future Directions

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python or comparable programming languages
  • Basic familiarity with Linux command-line operations

Target Audience

  • ML engineers responsible for deploying models into production
  • Data scientists seeking to manage reproducible experimental environments
  • AI developers creating scalable, containerized applications
 14 Hours

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