Get in Touch

Course Outline

Foundations of Containerization for AI & ML

  • Fundamental principles of containerization
  • The suitability of containers for ML workloads
  • Distinguishing between containers and virtual machines

Handling Docker Images and Containers

  • Comprehending images, layers, and registries
  • Overseeing containers for ML experimentation
  • Leveraging the Docker CLI for efficiency

Encapsulating ML Environments

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

Crafting Dockerfiles for Machine Learning

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

Encapsulating ML Models and Pipelines

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

Executing Containerized ML Services

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

Security and Compliance Factors

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

Production Deployment

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

Conclusion and Future Directions

Requirements

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

Target Audience

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

Number of participants


Price per participant

Testimonials (3)

Upcoming Courses

Related Categories