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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Exploring the mission and architectural design of Kubeflow
  • An overview of core components and the broader ecosystem
  • Deployment strategies and platform features

Utilizing the Kubeflow Dashboard

  • Navigating the user interface effectively
  • Managing notebooks and workspace configurations
  • Connecting storage solutions and data sources

Foundations of Kubeflow Pipelines

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

Training ML Models via Kubeflow

  • Patterns for distributed training
  • Leveraging TFJob, PyTorchJob, and other operators
  • Managing resources and autoscaling within Kubernetes

Serving Models with Kubeflow

  • An overview of KFServing / KServe
  • Deploying models using custom runtimes
  • Managing revisions, scaling policies, and traffic routing

Orchestrating ML Workflows on Kubernetes

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

Production ML Best Practices

  • Designing resilient workflow patterns
  • Establishing observability and monitoring frameworks
  • Diagnosing and resolving common Kubeflow challenges

Advanced Topics (Optional)

  • Configuring multi-tenant Kubeflow environments
  • Scenarios for hybrid and multi-cluster deployments
  • Extending Kubeflow capabilities with custom components

Summary and Future Directions

Requirements

  • A foundational understanding of containerized applications
  • Proficiency with basic command-line operations
  • Familiarity with core Kubernetes concepts

Target Audience

  • Machine learning practitioners
  • Data scientists
  • DevOps teams looking to adopt Kubeflow

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Provisional Upcoming Courses (Require 5+ participants)

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