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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.
- Exploration of deployment options and platform capabilities.
Utilizing the Kubeflow Dashboard
- Navigating the user interface.
- Managing notebooks and workspaces.
- Integrating storage and data sources.
Foundations of Kubeflow Pipelines
- Pipeline structure and component design principles.
- Creating pipelines using the Python SDK.
- Executing, scheduling, and monitoring pipeline runs.
Training ML Models on Kubeflow
- Distributed training methodologies.
- Leveraging TFJob, PyTorchJob, and other operators.
- Resource management and autoscaling within Kubernetes.
Model Serving with Kubeflow
- Introduction to KFServing / KServe.
- Deploying models with custom runtimes.
- Managing revisions, scaling, and traffic routing.
Managing ML Workflows on Kubernetes
- Versioning data, models, and artifacts.
- Integrating CI/CD into ML pipelines.
- Implementing security and role-based access control.
Best Practices for Production ML
- Designing reliable workflow patterns.
- Implementing observability and monitoring strategies.
- Troubleshooting common Kubeflow challenges.
Advanced Topics (Optional)
- Configuring multi-tenant Kubeflow environments.
- Hybrid and multi-cluster deployment scenarios.
- Extending Kubeflow with custom components.
Summary and Next Steps
Requirements
- A foundational understanding of containerized applications.
- Proficiency with basic command-line operations.
- Familiarity with core Kubernetes concepts.
Intended 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
