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Course Outline
Introduction to Kubeflow
- Gaining insight into the Kubeflow mission and architecture.
- Overview of core components and the broader ecosystem.
- Exploring deployment options and platform capabilities.
Working with the Kubeflow Dashboard
- Navigating the user interface effectively.
- Managing notebooks and workspaces.
- Integrating storage and data sources.
Kubeflow Pipelines Fundamentals
- Understanding pipeline structure and component design.
- Authoring pipelines using the Python SDK.
- Executing, scheduling, and monitoring pipeline runs.
Training ML Models on Kubeflow
- Implementing distributed training patterns.
- Utilizing TFJob, PyTorchJob, and other relevant operators.
- Managing resources and autoscaling within Kubernetes.
Model Serving with Kubeflow
- Overview of KFServing and 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 for ML pipelines.
- Implementing security and role-based access control.
Best Practices for Production ML
- Designing reliable workflow patterns.
- Ensuring observability and monitoring.
- Troubleshooting common Kubeflow issues.
Advanced Topics (Optional)
- Setting up 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.
- Practical experience with basic command-line workflows.
- Familiarity with core Kubernetes concepts.
Target Audience
- ML practitioners.
- Data scientists.
- DevOps teams new to the Kubeflow environment.
14 Hours
Testimonials (4)
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