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 Duration 14 hours

Course Outline

Introduction to Kubeflow

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

Interacting with the Kubeflow Dashboard

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

Fundamentals of Kubeflow Pipelines

  • Structuring pipelines and designing components
  • Creating pipelines using the Python SDK
  • Executing, scheduling, and monitoring pipeline executions

Training ML Models with Kubeflow

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

Serving Models with Kubeflow

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

Overseeing 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 reliable workflow patterns
  • Observability and monitoring strategies
  • Resolving common Kubeflow issues

Advanced Topics (Optional)

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

Conclusion and Next Steps

Requirements

  • A solid grasp of containerized applications
  • Familiarity with basic command-line operations
  • Basic knowledge of Kubernetes concepts

Target Audience

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

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