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Data Science
MLOps
Model Monitoring
Continuous tracking of model performance, data drift, and prediction quality in production.
Intent & Description
📋 Context
Models degrade over time due to data drift, concept drift, or changing conditions. Continuous monitoring ensures models remain effective.
Real-world Use Case
Production ML systems where model performance needs continuous validation and alerting.
Source
Advantages
- Early degradation detection
- Automated alerting
- Performance tracking
- Data drift detection
Disadvantages
- Infrastructure overhead
- False positives
- Monitoring complexity
- Resource costs
Implementation Example
# Model Monitoring Pattern class ModelMonitor: def __init__(self, model, baseline_metrics): self.model = model self.baseline = baseline_metrics
def check_performance(self, recent_predictions, actuals): current_metrics = self.calculate_metrics(recent_predictions, actuals)
drift = self.calculate_drift(self.baseline, current_metrics)
if drift > 0.1: # 10% drift threshold self.alert(f"Performance drift detected: {drift}")
def check_data_drift(self, new_data): # Compare new data distribution with training data drift_score = self.kolmogorov_smirnov_test( self.training_data, new_data ) return drift_score