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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Exploring the Kubeflow mission and architectural design
  • Review of core components and ecosystem landscape
  • Deployment strategies and platform capabilities

Interacting with the Kubeflow Dashboard

  • Navigating the user interface
  • Handling notebooks and workspace management
  • Connecting storage solutions and data sources

Kubeflow Pipelines Fundamentals

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

Training ML Models with Kubeflow

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

Serving Models via Kubeflow

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

Orchestrating ML Workflows on Kubernetes

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

Best Practices for Production ML

  • Designing reliable workflow patterns
  • Implementing observability and monitoring
  • Diagnosing and resolving common Kubeflow issues

Advanced Topics (Optional)

  • Multi-tenant Kubeflow configurations
  • Hybrid and multi-cluster deployment strategies
  • Extending Kubeflow with custom components

Summary and Next Steps

Requirements

  • Knowledge of containerized applications
  • Experience with fundamental command-line operations
  • Familiarity with key Kubernetes concepts

Target Audience

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

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