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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Comprehending the mission and architecture of Kubeflow
- Overview of core components and the broader ecosystem
- Deployment options and platform capabilities
Utilizing the Kubeflow Dashboard
- Navigation of the user interface
- Administration of notebooks and workspaces
- Integration of storage and data sources
Kubeflow Pipelines Fundamentals
- Pipeline structure and component design
- Creating pipelines with the Python SDK
- Running, scheduling, and monitoring pipeline executions
Training ML Models on Kubeflow
- Distributed training patterns
- Employing TFJob, PyTorchJob, and other operators
- Resource management and autoscaling within Kubernetes
Serving Models with Kubeflow
- Overview of KFServing / KServe
- Model deployment using custom runtimes
- Management of revisions, scaling, and traffic routing
Managing ML Workflows on Kubernetes
- Version control for data, models, and artifacts
- Integration of CI/CD for ML pipelines
- Security and role-based access control
Best Practices for Production ML
- Designing dependable workflow patterns
- Observability and monitoring strategies
- Resolution of common Kubeflow issues
Advanced Topics (Optional)
- Multi-tenant Kubeflow environments
- Hybrid and multi-cluster deployment scenarios
- Extension of Kubeflow via custom components
Summary and Next Steps
Requirements
- Comprehension of containerized applications
- Proficiency with fundamental command-line operations
- Acquaintance with Kubernetes concepts
Target Audience
- ML practitioners
- Data scientists
- DevOps teams new to Kubeflow
Testimonials (4)
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The knowledge and exchanges with Augustin