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ALIBI DETECT
Detect outliers, drift, and adversarial examples in ML models
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ABOUT
Machine learning models in production encounter data that differs from their training distribution — feature drift, label drift, outliers, and adversarial inputs. Without detection, these shifts silently degrade predictions and erode trust. Alibi Detect provides algorithms for online and offline drift detection, outlier detection across tabular, text, image, and time-series data, and adversarial example detection — all from a consistent Python API.
INSTALL
pip install alibi-detectINTEGRATION GUIDE
1. Monitor production ML models for data drift to trigger retraining before accuracy drops
2. Detect outliers in real-time sensor data streams for anomaly alerting
3. Identify adversarial inputs designed to manipulate model predictions
4. Run scheduled drift reports on batch inference pipelines using statistical tests
5. Detect concept drift in time-series forecasting models to flag regime changes
TAGS
pythonoutlier-detectiondrift-detectionadversarialmonitoringmlops