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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Understanding the mission and architectural design of Kubeflow
- Overview of core components and the broader ecosystem
- Exploring deployment options and platform capabilities
Interacting with the Kubeflow Dashboard
- Navigating the user interface effectively
- Administering notebooks and workspaces
- Integrating various storage solutions and data sources
Foundations of Kubeflow Pipelines
- Structuring pipelines and designing components
- Developing pipelines using the Python SDK
- Running, scheduling, and overseeing pipeline executions
Training ML Models on Kubeflow
- Implementing distributed training patterns
- Leveraging TFJob, PyTorchJob, and other operators
- Managing resources and autoscaling within Kubernetes
Serving Models with Kubeflow
- Introduction to KFServing / KServe
- Deploying models using custom runtimes
- Controlling revisions, scaling, and traffic routing
Orchestrating ML Workflows on Kubernetes
- Implementing versioning for data, models, and artifacts
- Incorporating CI/CD practices into ML pipelines
- Enforcing security through role-based access control
Best Practices for Production-Grade ML
- Architecting reliable workflow patterns
- Implementing observability and monitoring strategies
- Resolving common challenges encountered in Kubeflow
Advanced Topics (Optional)
- Configuring multi-tenant Kubeflow environments
- Handling hybrid and multi-cluster deployment scenarios
- Extending Kubeflow capabilities with custom components
Wrap-up and Recommended Next Steps
Requirements
- A foundational understanding of containerized applications
- Practical experience with basic command-line operations
- Familiarity with fundamental Kubernetes concepts
Intended Audience
- ML practitioners
- Data scientists
- DevOps teams new to Kubeflow
Testimonials (4)
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