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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Understanding the mission and architectural design of Kubeflow
  • Overview of core components and the broader ecosystem
  • Exploring deployment options and platform capabilities

Interacting with the Kubeflow Dashboard

  • Navigating the user interface effectively
  • Administering notebooks and workspaces
  • Integrating various storage solutions and data sources

Foundations of Kubeflow Pipelines

  • Structuring pipelines and designing components
  • Developing pipelines using the Python SDK
  • Running, scheduling, and overseeing pipeline executions

Training ML Models on Kubeflow

  • Implementing distributed training patterns
  • Leveraging TFJob, PyTorchJob, and other operators
  • Managing resources and autoscaling within Kubernetes

Serving Models with Kubeflow

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

Orchestrating ML Workflows on Kubernetes

  • Implementing versioning for data, models, and artifacts
  • Incorporating CI/CD practices into ML pipelines
  • Enforcing security through role-based access control

Best Practices for Production-Grade ML

  • Architecting reliable workflow patterns
  • Implementing observability and monitoring strategies
  • Resolving common challenges encountered in Kubeflow

Advanced Topics (Optional)

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

Wrap-up and Recommended Next Steps

Requirements

  • A foundational understanding of containerized applications
  • Practical experience with basic command-line operations
  • Familiarity with fundamental Kubernetes concepts

Intended Audience

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

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