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Duration 14 hours
Course Outline
Getting Started with Kubeflow
- Understanding the mission and architectural design of Kubeflow
- Overview of core modules and the broader ecosystem
- Deployment strategies and platform features
Interacting with the Kubeflow Dashboard
- Navigating the user interface
- Managing notebooks and workspaces
- Connecting storage and data sources
Essentials of Kubeflow Pipelines
- Pipeline architecture and component design
- Creating pipelines using the Python SDK
- Running, scheduling, and monitoring pipeline executions
Training ML Models on Kubeflow
- Distributed training methodologies
- Leveraging TFJob, PyTorchJob, and other operators
- Resource allocation and autoscaling within Kubernetes
Serving Models via Kubeflow
- Introduction to KFServing / KServe
- Deploying models with bespoke runtimes
- Handling revisions, scaling, and traffic distribution
Overseeing ML Workflows on Kubernetes
- Managing versions for data, models, and artefacts
- Incorporating CI/CD into ML pipelines
- Security and role-based access control
Best Practices for Production-Grade ML
- Designing resilient workflow structures
- Ensuring observability and monitoring
- Resolving common Kubeflow challenges
Advanced Concepts (Optional)
- Multi-tenant Kubeflow configurations
- Hybrid and multi-cluster deployment strategies
- Extending Kubeflow with custom components
Wrap-Up and Future Directions
Requirements
- A grasp of containerised applications
- Proficiency in basic command-line operations
- Knowledge of fundamental Kubernetes concepts
Target Audience
- Machine learning practitioners
- Data scientists
- DevOps teams new to the Kubeflow platform
Testimonials (4)
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