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📡 Model Monitoring in Production Explained

Watch live traffic drift away from a model's training distribution, trip an alert threshold, and trigger an automatic retrain that closes the feedback loop.

AI & Machine Learning3DAdvanced60 FPS

A stream of production requests flows into a deployed model while a live histogram of its incoming data drifts away from the fixed training baseline, driving a drift-score gauge that pages an alert and — if auto-retrain is armed — closes the feedback loop.

🔬 What It Demonstrates

The gap between a static training-time baseline (blue bars) and a shifting live distribution (orange bars) is measured every frame into a simplified PSI-style drift score, exactly the kind of statistic real monitoring pipelines track per feature.

🎮 How to Use

Raise the drift rate or live noise to push the orange histogram away from the blue one. Watch the gauge climb — cross the dashed threshold ring and the beacon alerts, triggering an automatic retrain pulse back to the model if auto-retrain is armed.

💡 Did You Know?

A Population Stability Index (PSI) below 0.1 is considered stable in most production ML systems; teams often set automatic retraining triggers around the 0.25 mark, the same three-zone green/amber/red logic this gauge visualizes.

⚙ Under the hood

Watch live traffic drift away from a model's training distribution, trip an alert threshold, and trigger an automatic retrain that closes the feedback loop.

machine learningmodel monitoringdata driftalertsretrainingproductionThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install