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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Grasping the Kubeflow mission and architectural design.
- Overview of core components and the broader ecosystem.
- Deployment strategies and platform capabilities.
Utilizing the Kubeflow Dashboard
- Navigating the user interface effectively.
- 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.
- Executing, scheduling, and monitoring pipeline runs.
Training ML Models on Kubeflow
- Distributed training patterns and strategies.
- 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 practices for ML pipelines.
- Security protocols and role-based access control.
Best Practices for Production ML
- Designing reliable workflow patterns.
- Observability and monitoring strategies.
- Troubleshooting common Kubeflow issues.
Advanced Topics (Optional)
- Multi-tenant Kubeflow environments.
- Hybrid and multi-cluster deployment scenarios.
- Extending Kubeflow with custom components.
Summary and Next Steps
Requirements
- A solid understanding of containerized applications.
- Experience with fundamental command-line workflows.
- Familiarity with core 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
