ML Workflow Pipeline

How to Use

1. Hover over a stage to see a tooltip card describing what happens at that step
2. Click a stage to highlight it with a gold border and read an organizational example at the bottom
3. Click Reset to clear all highlights and return to the default view

About

This simulation walks through the six stages of a machine learning pipeline applied to organizational analytics: defining the prediction problem, collecting graph and HR data, engineering features from network metrics, training a model, evaluating with fairness-aware metrics, and deploying with ongoing monitoring. The feedback arrow from Deploy back to Collect Data represents the retrain cycle that keeps models accurate as organizational dynamics shift over time.