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 Duration 14 hours

Course Outline

Foundations of Kubeflow

  • Exploring the goals and architectural design of Kubeflow
  • Surveying key components and the broader ecosystem
  • Examining deployment strategies and platform functionalities

Interacting with the Kubeflow Interface

  • Guided navigation of the user interface
  • Administration of notebooks and user workspaces
  • Connecting storage solutions and external data sources

Essentials of Kubeflow Pipelines

  • Understanding pipeline architecture and component structuring
  • Developing pipelines using the Python SDK
  • Overseeing execution, scheduling, and performance monitoring

Model Training within Kubeflow

  • Implementing distributed training methodologies
  • Leveraging operators such as TFJob, PyTorchJob, and others
  • Controlling resources and autoscaling features in Kubernetes

Serving Models with Kubeflow

  • An introduction to KFServing and KServe
  • Deploying models through custom runtime environments
  • Handling revisions, auto-scaling, and traffic distribution

Orchestrating ML Workflows on Kubernetes

  • Managing version control for data, models, and artifacts
  • Integrating CI/CD practices into ML pipelines
  • Enforcing security protocols and role-based access control

Production-Grade ML Best Practices

  • Architecting dependable workflow structures
  • Implementing observability and continuous monitoring
  • Resolving common Kubeflow operational challenges

Advanced Concepts (Optional)

  • Setting up multi-tenant Kubeflow instances
  • Deploying across hybrid and multi-cluster environments
  • Customizing Kubeflow with bespoke components

Wrap-Up and Future Directions

Requirements

  • Foundational knowledge of containerized application architectures
  • Practical experience with basic command-line operations
  • A working understanding of core Kubernetes concepts

Intended Audience

  • Machine Learning Engineers
  • Data Scientists
  • DevOps teams newly adopting Kubeflow

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