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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Comprehending the mission and architectural design of Kubeflow
  • Overview of core components and the broader ecosystem
  • Deployment strategies and platform functionalities

Interacting with the Kubeflow Dashboard

  • Navigating the user interface
  • Overseeing notebooks and workspace configurations
  • Connecting storage and data sources

Kubeflow Pipelines Fundamentals

  • Pipeline architecture and component design principles
  • Crafting pipelines using the Python SDK
  • Executing, scheduling, and overseeing pipeline executions

Training ML Models on Kubeflow

  • Distributed training methodologies
  • Utilizing TFJob, PyTorchJob, and related operators
  • Resource administration and autoscaling within Kubernetes

Serving Models with Kubeflow

  • Introduction to KFServing / KServe
  • Deploying models utilizing custom runtimes
  • Managing revisions, scaling, and traffic distribution

Administering ML Workflows on Kubernetes

  • Version control for data, models, and artifacts
  • Integrating CI/CD practices for ML pipelines
  • Security protocols and role-based access control

Best Practices for Production ML

  • Architecting reliable workflow patterns
  • Ensuring observability and continuous monitoring
  • Resolving common Kubeflow challenges

Advanced Topics (Optional)

  • Configuring multi-tenant Kubeflow environments
  • Hybrid and multi-cluster deployment scenarios
  • Extending Kubeflow capabilities with custom components

Summary and Future Directions

Requirements

  • A foundational grasp of containerized applications.
  • Practical experience with basic command-line operations.
  • Working knowledge of Kubernetes core concepts.

Target Audience

  • Machine Learning practitioners.
  • Data scientists.
  • DevOps teams beginning their journey with Kubeflow.

Testimonials (4)

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