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Performance monitoring

Take command of your AI’s performance. Define and track monitors that keep your models sharp, regardless of the feedback loop—because your outcomes depend on it.
Superwise performance monitoring dashboard displaying real-time model metrics and performance trends
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Prediction shift

I want to monitor output drift for prediction in all LTV models across all segments

Catch changes in your model’s predictions before they disrupt outcomes. Pinpoint where real-world data diverges from expectations to act swiftly and maintain control.

  • Get alerted when predictions shift on the segment level.
  • Identify increases and declines in prediction probability and confidence.
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Performance degradation

I want to monitor distribution shift for prediction probability in risk model across all segments & entire set

Detect performance dips—sudden or gradual—before they impact your business. Measure what matters with tools tailored to your goals.

  • Measure from a variety of out-of-the-box performance metrics such as recall, accuracy, ROC, etc.
  • Create and monitor custom performance metrics that reflect account business KPIs.
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Label shift

I want to monitor label shift for prediction in all CRO models across all segments

Detect label changes in your CRO models across all segments to ensure accuracy. Pinpoint shifts in real time and adjust to keep your AI grounded.

  • Pinpoint true label changes on the sub-population level.
  • Receive alerts if labels are not collected as expected downstream.
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Label Proportion

I want to monitor label proportion for prediction in 1 model across all segments

Prevent skewed labels from masking poor performance. Monitor distributions to catch imbalances and keep your models accurate.

  • Monitor binary & multi-class classification problems.
  • Identify accuracy vs. performance issues.
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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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