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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Comprehending the Kubeflow mission and architecture
- Overview of core components and the broader ecosystem
- Deployment options and platform capabilities
Interacting with the Kubeflow Dashboard
- Navigating the user interface
- Managing notebooks and workspaces
- Integrating storage solutions and data sources
Fundamentals of Kubeflow Pipelines
- Structuring pipelines and designing components
- Creating pipelines using the Python SDK
- Executing, scheduling, and monitoring pipeline executions
Training ML Models with Kubeflow
- Distributed training methodologies
- Leveraging TFJob, PyTorchJob, and other operators
- Resource management and autoscaling within Kubernetes
Serving Models with Kubeflow
- Introduction to KFServing / KServe
- Deploying models using custom runtimes
- Handling revisions, scaling, and traffic routing
Overseeing ML Workflows on Kubernetes
- Versioning data, models, and artifacts
- Integrating CI/CD for ML pipelines
- Security and role-based access control
Best Practices for Production ML
- Designing reliable workflow patterns
- Observability and monitoring strategies
- Resolving common Kubeflow issues
Advanced Topics (Optional)
- Multi-tenant Kubeflow environments
- Hybrid and multi-cluster deployment scenarios
- Extending Kubeflow with custom components
Conclusion and Next Steps
Requirements
- A solid grasp of containerized applications
- Familiarity with basic command-line operations
- Basic knowledge of Kubernetes concepts
Target Audience
- ML practitioners
- Data scientists
- DevOps teams new to Kubeflow
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
basic understanding of container/kubernetes and how they interact features of the openshift plattform