I want to monitor missing values for all entities in all LTV models across VIP customers
Identify missing values, outliers, and inconsistencies to keep your models running on clean, reliable data. Spot pipeline glitches early and maintain accurate predictions.
I want to monitor removed fields for S3 in 3 models across entire set
Align training and inference schemas across datasets, connectors, and streaming pipelines. Ensure consistency and visibility throughout all ML processes.
I want to monitor model stillness for prediction in 3 models across all segments and entire set
Monitor operational metrics in real time to detect variances before they impact performance. Catch model hiccups and technical bugs as they occur.
Track population shifts to identify growing, declining, or underperforming segments. Optimize your model to maximize impact across every sub-population.
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Easily get started with a free
community edition account.
!pip install superwise
import superwise as sw
project = sw.project("Fraud detection")
model = sw.model(project,"Customer a")
policy = sw.policy(model,drift_template)

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

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