Advanced Kubeflow Pipelines Workshop¶

This advanced-level workshop covers complex pipeline patterns, triggering mechanisms, resource optimization, and monitoring for Kubeflow Pipelines (KFP) in production environments.

Introduction¶

Kubeflow Pipelines provides a platform for building, deploying, and managing ML workflows on Kubernetes. While basic pipelines handle linear sequences of steps, production ML systems require more sophisticated patterns: conditional branching, parallel execution, event-driven triggers, and automated scaling.

In this workshop, you'll learn how to:

• Configure time-based and event-based pipeline triggers
• Implement complex workflow patterns (conditions, loops, fan-out/fan-in)
• Automate pipeline scheduling and retraining triggers
• Optimize resource allocation and enable auto-scaling
• Monitor, log, and debug production pipeline runs

Overview of the Exercises¶

Hands-On Sessions¶

Start with basic triggers, then proceed through the exercises in order:

• Exercise 1 - Basic Triggers
• Exercise 2 - Advanced Workflows
• Exercise 3 - Scheduling & Automation
• Exercise 4 - Optimization & Scaling
• Exercise 5 - Monitoring & Debugging

Directory Structure¶

kubeflow_advanced/
├── notebooks/
│   ├── 01_basic_triggers.ipynb
│   ├── 02_advanced_workflows.ipynb
│   ├── 03_scheduling_automation.ipynb
│   ├── 04_optimization_scaling.ipynb
│   └── 05_monitoring_debugging.ipynb
├── scripts/
│   ├── __init__.py
│   └── pipeline_helpers.py
├── requirements.txt
└── README.md

Prerequisites¶

• Python 3.9+
• KFP SDK v2.0+
• Access to a Kubernetes cluster with Kubeflow deployed
• Basic familiarity with Docker and container concepts
• Completion of the intermediate bike demand forecasting pipeline lab (recommended)

Technologies Used¶