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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Grasping the Kubeflow mission and underlying architecture
- Overview of core components and the broader ecosystem
- Exploring deployment options and platform features
Utilizing the Kubeflow Dashboard
- Navigating the user interface
- Managing notebooks and workspaces
- Connecting storage and data sources
Kubeflow Pipelines Basics
- Structuring pipelines and designing components
- Creating pipelines using the Python SDK
- Running, scheduling, and tracking pipeline executions
Training ML Models on Kubeflow
- Strategies for distributed training
- Utilizing TFJob, PyTorchJob, and other operators
- Handling resource management and autoscaling in Kubernetes
Model Serving with Kubeflow
- Introduction to KFServing / KServe
- Deploying models with custom runtimes
- Handling revisions, scaling, and traffic routing
Orchestrating ML Workflows on Kubernetes
- Versioning data, models, and artifacts
- Integrating CI/CD for ML pipelines
- Implementing security and role-based access control
Best Practices for Production ML
- Designing dependable workflow patterns
- Focusing on observability and monitoring
- Resolving common Kubeflow challenges
Advanced Topics (Optional)
- Setting up multi-tenant Kubeflow environments
- Managing hybrid and multi-cluster deployment scenarios
- Expanding Kubeflow with custom components
Recap and Future Directions
Requirements
- A foundational understanding of containerized applications
- Proficiency with basic command-line operations
- Familiarity with fundamental Kubernetes concepts
Target Audience
- ML practitioners
- Data scientists
- DevOps teams looking to integrate Kubeflow
Testimonials (4)
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