# Model Monitoring Drift

> Use when monitoring ML models for drift and degradation.

- Skill: `loopyluci/model-monitoring-drift` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/model-monitoring-drift`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/model-monitoring-drift/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/loopyluci/model-monitoring-drift

---


# Model Monitoring and Drift Detection

Monitoring ML models in production for data drift, concept drift, and performance degradation — from statistical drift detection through automated retraining triggers.

## When to Use

- ML models in production that may degrade over time
- Detecting when training data distribution differs from production
- Identifying when relationships between features and target change
- Automating retraining decisions based on drift signals
- Building ML observability and monitoring dashboards

## Drift Detection Methods

```python
DRIFT_TYPES = {
    'data_drift': 'Input feature distribution changes (e.g., user demographics shift)',
    'concept_drift': 'Relationship between features and target changes (e.g., buying behavior shifts)',
    'prediction_drift': 'Model output distribution shifts over time',
}

class DriftDetector:
    """Detect statistical drift in model inputs and outputs."""
    
    @staticmethod
    def psdi(data_current, data_reference, threshold: float = 0.1) -> Dict:
        """Population Stability Index — measures distribution shift."""
        from scipy import stats
        # Bin both distributions
        bins = np.histogram_bin_edges(data_reference, bins=10)
        ref_hist = np.histogram(data_reference, bins=bins)[0] + 1e-6
        cur_hist = np.histogram(data_current, bins=bins)[0] + 1e-6
        
        ref_pct = ref_hist / ref_hist.sum()
        cur_pct = cur_hist / cur_hist.sum()
        
        psi = np.sum((cur_pct - ref_pct) * np.log(cur_pct / ref_pct))
        return {'psi': round(psi, 4), 'drifted': psi > threshold}
    
    @staticmethod
    def ks_test(data_current, data_reference, threshold: float = 0.05) -> Dict:
        """Kolmogorov-Smirnov test for distribution difference."""
        from scipy import stats
        stat, p_value = stats.ks_2samp(data_reference, data_current)
        return {'statistic': round(stat, 4), 'p_value': round(p_value, 4), 'drifted': p_value < threshold}
```

## Common Pitfalls

1. **Alert fatigue** — every feature drifts slightly; set meaningful thresholds
2. **Seasonal drift** — natural cycles (holidays, weekends) trigger false alerts; model seasonality
3. **No ground truth** — concept drift needs labels to detect; may have delayed feedback
4. **Monitoring too many metrics** — focus on key features and overall prediction drift
5. **Reaction without analysis** — drift alert leads to automatic retraining; analyze root cause first

## Verification Checklist

- [ ] Baseline reference dataset established (training data or initial production window)
- [ ] Drift detection method chosen per feature type (PSI for categorical, KS for numeric)
- [ ] Alert thresholds configured (not too sensitive, not too lenient)
- [ ] Dashboard for visualizing drift over time
- [ ] Automated response defined (alert, retraining trigger, human review)
- [ ] Seasonality accounted for in drift calculations
- [ ] Concept drift detection (if ground truth available)