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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Comprehending the mission and architecture of Kubeflow
  • Overview of core components and the broader ecosystem
  • Deployment options and platform capabilities

Utilizing the Kubeflow Dashboard

  • Navigation of the user interface
  • Administration of notebooks and workspaces
  • Integration of storage and data sources

Kubeflow Pipelines Fundamentals

  • Pipeline structure and component design
  • Creating pipelines with the Python SDK
  • Running, scheduling, and monitoring pipeline executions

Training ML Models on Kubeflow

  • Distributed training patterns
  • Employing TFJob, PyTorchJob, and other operators
  • Resource management and autoscaling within Kubernetes

Serving Models with Kubeflow

  • Overview of KFServing / KServe
  • Model deployment using custom runtimes
  • Management of revisions, scaling, and traffic routing

Managing ML Workflows on Kubernetes

  • Version control for data, models, and artifacts
  • Integration of CI/CD for ML pipelines
  • Security and role-based access control

Best Practices for Production ML

  • Designing dependable workflow patterns
  • Observability and monitoring strategies
  • Resolution of common Kubeflow issues

Advanced Topics (Optional)

  • Multi-tenant Kubeflow environments
  • Hybrid and multi-cluster deployment scenarios
  • Extension of Kubeflow via custom components

Summary and Next Steps

Requirements

  • Comprehension of containerized applications
  • Proficiency with fundamental command-line operations
  • Acquaintance with Kubernetes concepts

Target Audience

  • ML practitioners
  • Data scientists
  • DevOps teams new to Kubeflow

Testimonials (4)

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