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Open-source library in Python letting you extract metafeatures from unstructured data.
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Everything you need to know about all types of drift including concept drift, data drift, and model drift.
A framework for building, testing, and implementing a robust model monitoring strategy.
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Elemeta is an open-source library in Python for metafeature extraction. With it, you will be able to explore, monitor, and extract features from unstructured data through enriched tabular representations. It provides a straightforward Python API for metadata extraction from unstructured data like text and images.
Monitor contextual metafeatures from Elemeta with Superwise to identify changes in the input or output behaviors of your end users and models.
Monitor statistical metafeatures from Elemeta with Superwise to identify changes in the distribution and representation of your NLP use case.
!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.