TemplateFREE⏱️ 35 min
ML Model Monitoring Template for AI Products
An ML model monitoring and drift detection plan template covering performance tracking, data drift detection, prediction drift, alerting thresholds,...
Updated 2026-03-04
ML Model Monitoring
| # | Metric | Target | Current | Progress % | Owner | Status | |
|---|---|---|---|---|---|---|---|
| 1 | 140 | ||||||
| 2 | 98 | ||||||
| 3 | 84 | ||||||
| 4 | 75 | ||||||
| 5 | 75 |
#1
140
#2
98
#3
84
#4
75
#5
75
Edit the values above to try it with your own data. Your changes are saved locally.
Get this template
Choose your preferred format. Google Sheets and Notion are free, no account needed.
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. ---
Explore More Templates
Browse our full library of PM templates, or generate a custom version with AI.