# Model Monitoring

> Continuous tracking of model performance, data drift, and prediction quality in production.

- **Category**: Data Science
- **Subcategory**: MLOps
- **Canonical URL**: https://designpattern.fyi/patterns/model-monitoring/

---

## Description
**Context**: Models degrade over time due to data drift, concept drift, or changing conditions. Continuous monitoring ensures models remain effective.


## Use Cases
Production ML systems where model performance needs continuous validation and alerting.



## Implementation Example

```python
# 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
```



## Trade-offs


### Advantages

- - Early degradation detection

- - Automated alerting

- - Performance tracking

- - Data drift detection




### Considerations & Drawbacks

- - Infrastructure overhead

- - False positives

- - Monitoring complexity

- - Resource costs







---
**Reference**: [Original Source](https://docs.aws.amazon.com/sagemaker/)