Observability
Monitor Models In Production
Real-time observability for computer vision models. Detect anomalies, track drift, and close the feedback loop automatically.
24/7 Monitoring
- Anomaly Rate: <1%
- Latency P99: <50ms
See It In Action Documentation
Anomaly Detection
Catch failures before users do
Automatic 24/7 monitoring flags anomalous inputs and low-confidence predictions. Reveal blindspots and discover edge cases in production data.
Low Confidence Alerts
Flag predictions below confidence thresholds automatically.
Distribution Shift
Detect when production data drifts from training data.
Novel Patterns
Discover previously unseen data patterns and edge cases.
FLAGGED PREDICTIONS Last hour
23%
45%
31%
89%
18%
52%
6 anomalies detected
Live Dashboard
Real-time prediction insights
Filter millions of inferences to identify the top anomalies. From edge case detection to training dataset integration in seconds.
- Total Inferences: 1.2M Last 24 hours
- Avg Latency: 47ms P50 response time
- Accuracy: 99.1% Based on reviewed predictions
MONITORING METHODS
# Monitor from file
department.monitor(
"image.jpg",
tags=["production"]
)
# Monitor from bytes
department.monitor_bytes(
"frame.jpg",
raw_bytes
)
# Get deployment stats
stats = department.get_stats(
window="24h"
)
# Access metrics
print(stats.predictions)
print(stats.reviews)
print(stats.latency_p99)
DRIFT DETECTION vs. training baseline
- Mean Brightness: 127.3 ↑7.4%
- Contrast: 0.4 ↓6.7%
- Class Distribution: 23.1% ↓10.5%
- Object Size (avg): 156.0px ↑9.9%
- Confidence (avg): 0.8 ↓7.7%
3 metrics drifting Last 7 days
Data Drift
Track distribution changes
Compare production data against your training baseline. Get alerted when distributions shift beyond acceptable thresholds.
- Image statistics (brightness, contrast, blur)
- Class distribution changes
- Confidence score degradation
- Custom threshold alerts
Continuous Improvement
Close the feedback loop
Convert production failures into training data. Review predictions, attach to datasets, and trigger retraining automatically.
- Monitor Production inference
- Detect Anomalies & drift
- Review Verify & label
- Retrain Improve model
Continuous Training
Automatically trigger retraining when prediction review thresholds are met. Keep models fresh with production data.
department.toggle_continuous_training()
Continuous Deployment
Manage model promotion policies between staging and production. Shadow model support for A/B testing.
department.set_shadow_model(new_version)
Ready to monitor your models?
Start detecting anomalies, tracking drift, and improving models with production feedback.