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Course Outline

Introduction to Containerization for AI & ML

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

Working with Docker Images and Containers

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

Packaging ML Environments

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

Building Dockerfiles for Machine Learning

  • Designing Dockerfiles suitable for ML projects
  • Best practices for ensuring performance and maintainability
  • Leveraging multi-stage builds

Containerizing ML Models and Pipelines

  • Encapsulating trained models into containers
  • Strategies for managing data and storage
  • Deploying reproducible end-to-end workflows

Running Containerized ML Services

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

Security and Compliance Considerations

  • Establishing secure container configurations
  • Managing access controls and credentials
  • Safeguarding confidential ML assets

Deploying to Production Environments

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

Summary and Next Steps

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 focused on model deployment to production
  • Data scientists seeking to manage reproducible experiment environments
  • AI developers constructing scalable, containerized applications
 14 Hours

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