It's the gradual degradation of a model's performance in production, usually because real-world data stops resembling the data it was trained on (data drift) or because the relationship between variables changes (concept drift). A model validated today can become unreliable within months without anyone noticing if it isn't monitored.

Drift Detection & Monitoring

What's included

PDF with the monitoring methodology: which metrics to watch (data drift, concept drift, accuracy degradation), how often, and reference alert thresholds
Excel with a per-model metrics log template over time
Response protocol when drift is detected: who decides whether the model gets retrained, retired, or stays in production under enhanced oversight
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Why this document exists

A model is validated once, but operates continuously in a world that changes — new user behavior patterns, seasonal shifts, external events that alter the relationship between the variables the model learned. Without active monitoring, the first sign something's wrong is usually a customer complaint or an audit finding, by which point the damage is done. This document turns "drift" into a concrete monitoring process, with thresholds and a clear response protocol.

Frequently asked questions

What is model drift?

It's the gradual degradation of a model's performance in production, usually because real-world data stops resembling the training data, or because the relationship between variables changes. A model validated today can become unreliable within months without anyone noticing if it isn't monitored.

Does this replace the Audit Dashboard?

No, they're complementary. The Audit Dashboard reports program-level governance indicators; this plan focuses specifically on each model's technical performance in production over time.

What format is it delivered in?

PDF with the monitoring methodology and alert thresholds, plus Excel with a per-model metrics log template.