Kubeflow Essentials: Build, Train & Serve with Kubernetes Training Course

Introduction to Kubeflow for Government

• Understanding the Mission and Architecture of Kubeflow
• Overview of Core Components and Ecosystem
• Deployment Options and Platform Capabilities

Working with the Kubeflow Dashboard for Government

• Navigation of User Interface
• Managing Notebooks and Workspaces
• Integrating Storage and Data Sources

Kubeflow Pipelines Fundamentals for Government

• Structure and Component Design of Pipelines
• Authoring Pipelines with Python SDK
• Executing, Scheduling, and Monitoring Pipeline Runs

Training Machine Learning Models on Kubeflow for Government

• Patterns for Distributed Training
• Using TFJob, PyTorchJob, and Other Operators
• Resource Management and Autoscaling in Kubernetes

Model Serving with Kubeflow for Government

• Overview of KFServing / KServe
• Deploying Models with Custom Runtimes
• Managing Revisions, Scaling, and Traffic Routing

Managing Machine Learning Workflows on Kubernetes for Government

• Versioning Data, Models, and Artifacts
• Integrating CI/CD for ML Pipelines
• Security and Role-Based Access Control

Best Practices for Production Machine Learning for Government

• Designing Reliable Workflow Patterns
• Observability and Monitoring
• Troubleshooting Common Kubeflow Issues

Advanced Topics (Optional) for Government

• Multi-Tenant Kubeflow Environments
• Hybrid and Multi-Cluster Deployment Scenarios
• Extending Kubeflow with Custom Components

Summary and Next Steps for Government