Design a Real-time Model Monitoring and Drift Detection System — System Design Interview Practice
Design a monitoring system that detects data drift, concept drift, and model performance degradation in production ML models, triggering automated alerts and retraining workflows. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Monitor input data distribution for driftRequirement
- Detect concept drift and prediction quality degradationRequirement
- Track model performance metrics in real-timeRequirement
- Compute statistical tests for drift detectionRequirement
- Alert on significant drift or performance dropsRequirement
- Trigger automated retraining pipelinesRequirement
- Store prediction logs for analysisRequirement
- Provide monitoring dashboards with drill-downRequirement
- mlopsConcept to explore
- model monitoringConcept to explore
- drift detectionConcept to explore
- observabilityConcept to explore
- alertingConcept to explore
- production mlConcept to explore