ML Drift and Cardinality Monitoring

Metric monitors now support drift detection and cardinality tracking for ML models. Monitor distribution shifts on the input and output tables of your ML models using PSI, KS Test, or JS Divergence, and surface new or missing distinct values with dedicated cardinality metrics.

Drift metrics compare recent data against a configurable baseline so your team can catch distribution shifts over time. Cardinality metrics flag when new values appear or existing ones go missing in key fields.

These join the existing regression and classification metrics in the metric selector, giving your team a single place to monitor data pipelines, AI, and ML.

Learn more here: https://docs.getmontecarlo.com/docs/available-metrics#ml-metrics