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CENSIUS
Full-stack AI observability from model to business impact
ABOUT
ML models degrade silently in production — data drift, concept drift, and edge cases accumulate without any visible signal until users complain. Censius continuously monitors model inputs, predictions, and outcomes to detect drift and performance degradation the moment they appear. Its explainability engine surfaces which features drove each prediction, and the root cause analysis module traces performance drops back to their source data changes. Teams get early warnings before model quality affects business metrics.
INSTALL
pip install censiusINTEGRATION GUIDE
1. Monitor production ML models for data drift and concept drift with automated alerts before accuracy drops
2. Explain individual predictions with feature attribution to debug unexpected model behavior
3. Trace root causes of model degradation back to specific data pipeline changes or feature shifts
4. Track LLM output quality over time with custom metrics for helpfulness, safety, and consistency
5. Build compliance dashboards that document model behavior for regulatory audits and fairness reviews
TAGS
monitoringobservabilityexplainabilitymlopsllmpythonmodel-monitoringroot-cause-analysis