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ML Model Monitoring Template for AI Products
An ML model monitoring and drift detection plan template. Covers performance tracking, data drift detection, prediction drift, and alerting thresholds.
IPBy IdeaPlan Editorial · Methodology
Updated 2026-03-04
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Frequently Asked Questions
What is the most important thing to monitor for a production ML model?+
Monitor the ground truth performance metric (accuracy, AUC, F1) first, data drift second, and prediction drift third. Performance is the ultimate indicator, but it often has a delay (you need ground truth labels). Data drift is the leading indicator that predicts future performance degradation. The [model accuracy score](/metrics/model-accuracy-score) metric provides guidance on measurement methodology.
How quickly does model performance degrade in practice?+
It varies widely by domain. Models that depend on user behavior (recommendations, churn) typically show measurable drift within 1-3 months as product changes shift behavior patterns. Models that depend on stable physical data (image classification, sensor data) may remain stable for 6-12 months. Monitor actively and let the data tell you rather than assuming a fixed decay rate.
What is PSI (Population Stability Index) and when should I use it?+
PSI measures how much a feature distribution has shifted from a reference distribution. PSI < 0.1 indicates no significant shift. PSI 0.1-0.2 indicates moderate shift worth investigating. PSI > 0.2 indicates significant shift requiring action. Use PSI for numerical and categorical features. It is more interpretable than KS tests for business stakeholders because it produces a single number on a consistent scale.
Should I retrain on a fixed schedule or based on triggers?+
Trigger-based retraining is better because it avoids unnecessary retraining (waste) and catches sudden degradation faster than a fixed schedule. However, set a maximum interval (e.g., 90 days) as a backstop. Some drift is gradual enough that no single daily measurement crosses the threshold, but the cumulative shift over months degrades performance. The [AI PM Handbook](/ai-guide) covers retraining strategy in its MLOps chapters.
How do I monitor generative AI models that do not have ground truth labels?+
Use proxy metrics: user feedback (thumbs up/down ratios), downstream behavior (did the user accept the suggestion?), and automated quality checks (factual consistency scores, [hallucination rate](/metrics/hallucination-rate), response relevance classifiers). Monitor output distribution metrics (response length, confidence scores, topic distribution) for prediction drift. Periodic human evaluation on random samples provides the closest approximation to ground truth. ---
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