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 Duration 14 hours

Course Outline

Introduction to Kubeflow

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

Utilizing the Kubeflow Dashboard

  • Navigating the user interface
  • Managing notebooks and workspaces
  • Connecting storage and data sources

Kubeflow Pipelines Basics

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

Training ML Models on Kubeflow

  • Distributed training methodologies
  • Utilizing TFJob, PyTorchJob, and other operators
  • Resource management and autoscaling within Kubernetes

Model Serving via Kubeflow

  • Introduction to KFServing / KServe
  • Deploying models using custom runtimes
  • Handling revisions, scaling, and traffic routing

Managing ML Workflows on Kubernetes

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

Best Practices for Production ML

  • Designing dependable workflow patterns
  • Observability and monitoring
  • Resolving common Kubeflow issues

Advanced Topics (Optional)

  • Multi-tenant Kubeflow environments
  • Hybrid and multi-cluster deployment cases
  • Extending Kubeflow with custom components

Conclusion and Next Steps

Requirements

  • A foundational grasp of containerized applications
  • Experience with basic command-line interfaces
  • Knowledge of Kubernetes concepts

Target Audience

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

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