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 Duration 14 hours

Course Outline

Introduction to Kubeflow

  • Exploring the mission and architecture of Kubeflow
  • Overview of core components and the broader ecosystem
  • Deployment strategies and platform capabilities

Interacting with the Kubeflow Dashboard

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

Foundations of Kubeflow Pipelines

  • Pipeline structure and component design principles
  • Developing pipelines using the Python SDK
  • Execution, scheduling, and monitoring of pipeline runs

Training ML Models on Kubeflow

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

Serving Models with Kubeflow

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

Overseeing ML Workflows on Kubernetes

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

Best Practices for Production ML

  • Designing dependable workflow patterns
  • Implementing observability and monitoring
  • Resolving common Kubeflow issues

Advanced Topics (Optional)

  • Setting up multi-tenant Kubeflow environments
  • Hybrid and multi-cluster deployment strategies
  • Extending Kubeflow through custom components

Conclusion and Future Steps

Requirements

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

Target Audience

  • Machine Learning practitioners
  • Data scientists
  • DevOps teams exploring Kubeflow for the first time

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