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📊 MLOps Model Monitoring

Interactive 3D data-distribution plot where shifting an incoming data stream over simulated time triggers drift-detection alerts before model accuracy visibly degrades.

AI & Machine Learning3DModerate60 FPS

An interactive 3D data-distribution plot: a live production stream flows in next to a frozen training reference, and shifting it over simulated time triggers a drift-detection alert before the model's rolling accuracy visibly degrades.

🔬 What It Demonstrates

Data drift (shifting input distribution) and concept drift (a changing input-output relationship) are distinct failure modes. A statistical drift score reacts to distribution shift immediately, while accuracy only drops once the true and model decision boundaries genuinely disagree.

🎮 How to Use

Increase data drift magnitude to shift the incoming stream, and concept drift angle to rotate the true decision boundary away from the model's frozen one. Watch the drift score cross its alert threshold, then compare how long it takes rolling accuracy to catch up.

💡 Did You Know?

Because ground-truth labels are often delayed in production, most MLOps teams monitor distribution statistics like PSI or KS-distance as an early-warning proxy long before a true accuracy metric is even computable.

⚙ Under the hood

Interactive 3D live-metrics landscape where shifting incoming data distribution shows how data drift and concept drift alarms trigger before production model accuracy collapses.

mlopsmodel-driftconcept-driftmodel-monitoringmachine-learning-opsproduction-mlai-ml

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