Elemeta is an open-source Python library that transforms raw text and image data into structured, tabular metafeatures—ready for monitoring. With its clean Python API, it plugs seamlessly into SUPERWISE, helping you track NLP signals, surface drift, and monitor model behavior with clarity.
I want to monitor average values for SentimentPolarity in all social pipelines across all segments & entire set
SUPERWISE uses contextual metafeatures from Elemeta to track changes in user input or model output—so you can detect spikes, drifts, and outliers before they impact production.
I want to monitor max value for OutOfVocabularyCount in video search model across teens segment
Use statistical metafeatures to track shifts in population, spot underperforming segments, and catch subtle changes in data distribution—before they chip away at model accuracy.
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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.