Decision Signals in Production: From Notebooks to Governed Evidence

The Training-Serving Skew Problem

Training-serving skew is the #1 cause of silent model degradation in production. It occurs when the features the model saw during training differ from those received at inference time. Causes are multiple: transformations implemented differently in training (Python/pandas) vs serving (Java/SQL), features computed with future data during training (temporal data leakage), or simply bugs in feature engineering logic that go undetected until production.

Governed Evidence: The Skew Solution

A governed evidence layer centralizes definitions, freshness, ownership, quality, and traceability of signals. What matters to the buyer is not the technical provider, but that the same signal has consistent meaning in simulation, review, recommendation, and auditability.

Signal Monitoring and Governance

Signals that inform a critical decision are assurance assets: they require ownership, documentation, traceability, quality, freshness, and usage criteria. Monitoring detects degraded or missing evidence; governance defines who can rely on each signal, under which limits, and with what review.