Observability & Evaluation · Software component
Data Drift Detector
Software componentObservability & EvaluationObservability & Evaluationarc:DataDriftDetector
A monitoring component that compares production input-feature, prediction and ground-truth label distributions with a reference dataset using statistical tests or distance metrics to detect distribution shift.
Responsibility. Detects statistically significant shifts in input, prediction or label distributions relative to a reference baseline.
Also known as: Feature drift detector, Prediction drift detector, Label drift detector, Query distribution drift detection, Prediction distribution drift detection
Relationships
is invoked by dependency
- Production Quality Monitor abstract Ref8.02
reads dependency
sends data to dynamic
triggers dynamic
monitors assurance
Design guidance
- SHOULD identify the specific drifting features before remediation (retraining, correction layer, more data or threshold adjustment).
- SHOULD require both statistical significance and practical significance before alerting on input drift (Ref8.04).
- SHOULD test drift detection on historical data and track drift causes over time (Ref8.04).
Quantitative guidance
As stated by the sources; verify before use.
- Input drift alert at KS p < 0.05 plus > 20% mean shift; e.g., mean query length rising from 45 to 80+ tokens (Ref8.04).
- Prediction drift flagged at > 15% shift in any category (e.g., TaskA 60% -> 45%, TaskB 25% -> 40%) (Ref8.04).
Classification
- Patterns
- Feature distribution driftPrediction driftLabel driftKolmogorov-Smirnov testJensen-Shannon divergence
- Technologies
- Evidently AISciPy
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
- Risks mitigated
- Data distribution shiftModel seeing data it was not trained onUser population, seasonal, bot/spam or domain shifts degrading agent performance
- Frameworks & regulations
- NIST AI RMF: MEASUREFDA guidance on AI/ML in medical devices (real-world performance monitoring)
Sources
- Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
- Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.
- Ch10.3: T. Nguyen, "RLHF Methodology," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.3. ISBN: 9798244538229.
- Ref8.02: "Machine Learning Monitoring in Production," unpublished reference note (02-ML-Monitoring-Production.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref8.04: "Data Quality and Drift Detection for Agent Systems," unpublished reference note (04-Data-Quality-Drift-Detection.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref9.07: "Risk Assessment and Management for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/07-Risk-Assessment-Management.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note