Drift monitoring

Stay ahead of data, concept, and model drift. Know precisely when to optimize and retrain with tools that keep you in control.
Drift Monitoring
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Feature stability

I want to monitor distribution shift for top 5 important features in all fraud models across entire set

Keep top feature contributions consistent, ensuring your models deliver reliable ML predictions every time.

  • Track feature importance over time.
  • Track feature importance across datasets.
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Training-serving skew

I want to monitor training-serving skew in 4 models across entire set

Quickly identify mismatches between training and production data to prevent accuracy degradation.

  • Identify training datasets not representative of your population.
  • Identify feature implementation issues that skewed inferences or scales.
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Dataset shift

I want to monitor input drift for all entities in all fraud models across all segments comparing last week to same period last month

Keep your models performing optimally by detecting dataset shifts in real-time.

  • Ensure data is being updated on schedule.
  • Identify peaks and drops in volume.
  • Pinpoint model stillness.
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Try the community edition

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Sign up for SUPERWISE

Easily get started with a free
community edition account.

				
					!pip install superwise

Build your project

				
					import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a") 
policy = sw.policy(model,drift_template)

Start monitoring

Fraud detection

Entire population drift – high probability of concept drift. Open incident investigation →

Fraud detection

Segment “tablet shoppers” drifting.
Split model and retrain.

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