Feature drift — what it is, how it differs from data drift, and how to monitor it
A practitioner's guide to feature drift in production ML systems, covering definitions, the relationship to data drift, monitoring metrics, and response thresholds.
3 docs tagged with "machine-learning"
View all tagsML Observability — Core Concepts
Data drift, concept drift, metrics, telemetry, and the three pillars of ML observability.
ML Observability — Debugging Production Models & Why It's Different
A practitioner's guide to diagnosing underperforming ML models in production and understanding why ML observability is a distinct discipline from traditional software monitoring.