Structured Event Logging for Tracking Model Behavior Under Distributional Drift

Distributional drift poses fundamental challenges for deployed machine learning systems. While drift detection methods exist, current monitoring approaches lack systematic mechanisms for recording relationships between drift events, intervention decisions, and performance outcomes. We propose a structured event logging methodology that organizes the drift management lifecycle through an event taxonomy spanning detection, intervention, and monitoring phases. We evaluate this approach using 5-fold temporal cross-validation across three domains (prediction markets, electricity pricing, weather forecasting) with varying drift characteristics. Our results show that structured event logs can support retrospective analysis of drift patterns and their relationship to model performance across different drift regimes.