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Course Outline

Fundamentals of Containerization in AI & ML

  • Essential principles of container technology
  • The advantages of containers for ML workloads
  • Distinguishing features of containers versus virtual machines

Managing Docker Images and Containers

  • Comprehending images, layer architecture, and registries
  • Oversight of containers for ML experimentation
  • Efficient utilization of the Docker command-line interface

Encapsulating ML Environments

  • Readying ML codebases for containerization
  • Oversight of Python environments and dependency management
  • Incorporating CUDA and GPU capabilities

Creating Dockerfiles for Machine Learning

  • Architecting Dockerfiles for ML projects
  • Best practices for ensuring performance and maintainability
  • Leveraging multi-stage build processes

Containerizing ML Models and Pipelines

  • Encapsulating trained models within containers
  • Oversight of data and storage strategies
  • Implementing reproducible end-to-end workflows

Operationalizing Containerized ML Services

  • Exposing API endpoints for model inference
  • Scaling services using Docker Compose
  • Monitoring runtime performance and behavior

Security and Compliance Implications

  • Implementing secure container configurations
  • Management of access controls and credentials
  • Protecting sensitive ML assets

Production Deployment Strategies

  • Publishing images to container registries
  • Deploying containers in on-premise or cloud infrastructures
  • Version control and updating of production services

Recap 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 to production
  • Data scientists focused on maintaining reproducible experiment environments
  • AI developers constructing scalable, container-based applications
 14 Hours

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