Data monitoring

Take charge of AI monitoring with a system built for adaptability. Launch fast with ready-made metrics, policies, and alerts—or fine-tune everything to fit your exact needs.

Data quality

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.

Dataset schema

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.

Data activity

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.

Population changes

I want to monitor growing predictions in churn models across NA & EU accounts

Track population shifts to identify growing, declining, or underperforming segments. Optimize your model to maximize impact across every sub-population.

Try the community edition

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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

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