The Problem 😅
You've probably been here:
- Deploy ML model ✅
- Model works great initially ✅
- Stakeholders are happy ✅
- Then... 📉 silent degradation
- Business metrics drop 📊
- "Why didn't we know sooner?" 🤔
Traditional monitoring doesn't work for ML models.
The Architecture 🏗️
Built a 3-layer monitoring system:
Layer 1: Models & Data 🤖
┌─────────────────┐ ┌─────────────────┐
│ ML Model │ │ Data Storage │
│ (FastAPI) │◄───┤ (PostgreSQL/S3) │
└─────────────────┘ └─────────────────┘
Layer 2: Processing ⚙️
┌─────────────────┐ ┌─────────────────┐
│ Drift Detection │ │ Orchestration │
│ (Evidently AI) │◄───┤ (Prefect) │
└─────────────────┘ └─────────────────┘
Layer 3: Alerts & Viz 📊
┌─────────────────┐ ┌─────────────────┐
│ Dashboards │ │ Alerts │
│ (Grafana) │◄───┤ (Slack/PagerDuty)│
└─────────────────┘ └─────────────────┘
Key Monitoring Metrics 📈
🎯 Prediction Drift
Detect when model outputs change distribution:
python
from evidently.metrics import DatasetDriftMetric
def check_prediction_drift(reference, current):
metric = DatasetDriftMetric()
result = metric.calculate(reference, current)
return result.drift_detected
📊 Feature Drift
Monitor input feature distributions:
Mean/median shifts
Standard deviation changes
Quantile-based detection
❌ Data Quality
Real-time validation:
Missing value %
Outlier detection
Schema changes
📉 Performance Metrics
When ground truth available:
Accuracy trends
F1-score evolution
Business KPI correlation
Implementation Example 💻
pythonclass MLMonitor:
def __init__(self, reference_data):
self.reference_data = reference_data
self.slack_webhook = os.getenv('SLACK_WEBHOOK')
def monitor_predictions(self, current_data):
"""Main monitoring function"""
# 1. Check for drift
drift_result = self.check_drift(current_data)
# 2. Validate data quality
quality_result = self.check_quality(current_data)
# 3. Send alerts if needed
if drift_result['drift_detected']:
self.send_alert(f"🚨 Drift detected: {drift_result['drift_score']:.3f}")
# 4. Update dashboards
self.update_metrics(drift_result, quality_result)
def check_drift(self, current_data):
"""Drift detection with Evidently"""
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
report = Report(metrics=[DataDriftPreset()])
report.run(self.reference_data, current_data)
return report.as_dict()
def send_alert(self, message):
"""Send Slack notification"""
import requests
payload = {
"text": message,
"channel": "#ml-alerts",
"username": "ML Monitor Bot"
}
requests.post(self.slack_webhook, json=payload)
Results 📊
After implementing this system:
MetricBeforeAfterDetection Time2-3 days2-3 hoursMonthly Incidents83False Positive Rate40%5%Stakeholder Confidence😐😍
Tech Stack Choices 🛠️
Why Evidently AI?
Open source & flexible
Excellent drift algorithms
Great documentation
Active community
Why Grafana?
Beautiful dashboards
Real-time capabilities
PostgreSQL integration
Industry standard
Why Prefect over Airflow?
Modern Python-first approach
Better error handling
Easier Kubernetes deployment
Superior observability
Lessons Learned 💡
✅ What Worked
Start simple - Basic drift detection first
Tune thresholds - Avoid alert fatigue
Pretty dashboards - Stakeholders love visuals
Automation - Let system handle simple fixes
❌ What Failed
Too many alerts initially - Alert fatigue is real
Complex metrics upfront - Confused the team
Manual processes - Doesn't scale
What's Next? 🔮
Planning to add:
Automated retraining triggers
A/B testing integration
Cost monitoring per prediction
Explainability tracking with SHAP
Conclusion 🎉
ML monitoring isn't optional anymore. This architecture has:
Caught issues 10x faster
Reduced incidents by 60%
Improved stakeholder trust
Made our ML systems actually reliable
Key takeaway: Treat monitoring as a first-class citizen in your ML pipeline.
What monitoring challenges are you facing? Share in the comments!
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