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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Comprehending the mission and architectural design of Kubeflow
- Overview of core components and the broader ecosystem
- Deployment strategies and platform functionalities
Interacting with the Kubeflow Dashboard
- Navigating the user interface
- Overseeing notebooks and workspace configurations
- Connecting storage and data sources
Kubeflow Pipelines Fundamentals
- Pipeline architecture and component design principles
- Crafting pipelines using the Python SDK
- Executing, scheduling, and overseeing pipeline executions
Training ML Models on Kubeflow
- Distributed training methodologies
- Utilizing TFJob, PyTorchJob, and related operators
- Resource administration and autoscaling within Kubernetes
Serving Models with Kubeflow
- Introduction to KFServing / KServe
- Deploying models utilizing custom runtimes
- Managing revisions, scaling, and traffic distribution
Administering ML Workflows on Kubernetes
- Version control for data, models, and artifacts
- Integrating CI/CD practices for ML pipelines
- Security protocols and role-based access control
Best Practices for Production ML
- Architecting reliable workflow patterns
- Ensuring observability and continuous monitoring
- Resolving common Kubeflow challenges
Advanced Topics (Optional)
- Configuring multi-tenant Kubeflow environments
- Hybrid and multi-cluster deployment scenarios
- Extending Kubeflow capabilities with custom components
Summary and Future Directions
Requirements
- A foundational grasp of containerized applications.
- Practical experience with basic command-line operations.
- Working knowledge of Kubernetes core concepts.
Target Audience
- Machine Learning practitioners.
- Data scientists.
- DevOps teams beginning their journey with Kubeflow.
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