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.

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.

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.

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.

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.

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					!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

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