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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Understanding the mission and architecture of Kubeflow.
- 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 solutions and data sources.
Kubeflow Pipelines Fundamentals
- Pipeline structure and component design principles.
- Creating pipelines using the Python SDK.
- Execution, scheduling, and monitoring of pipeline runs.
Training ML Models on Kubeflow
- Distributed training patterns.
- Utilizing TFJob, PyTorchJob, and other operators.
- Resource management and autoscaling strategies 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 processes for ML pipelines.
- Security measures and role-based access control.
Best Practices for Production ML
- Designing reliable workflow patterns.
- Implementing observability and monitoring.
- Troubleshooting common Kubeflow issues.
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 solid grasp of containerized applications.
- Proficiency with basic command-line operations.
- Knowledge of core Kubernetes concepts.
Target Audience
- ML practitioners.
- Data scientists.
- DevOps teams new to the Kubeflow platform.
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
