Supervisez vos modèles en production

Observabilité en temps réel pour les modèles de vision par ordinateur. Détectez les anomalies, suivez le drift et fermez la boucle de feedback automatiquement.

24/7 Monitoring

<1% Anomaly Rate
<50ms Latency P99
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%

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.

1.2M

Total Inferences Last 24 hours

47ms

Avg Latency P50 response time

99.1%

Accuracy Based on reviewed predictions

MONITORING METHODS

# Monitor from file
deployment.monitor(
  "image.jpg",
  tags=["production"]
)

# Monitor from bytes
deployment.monitor_bytes(
  "frame.jpg",
  raw_bytes
)

# Get deployment stats
stats = deployment.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

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.

deployment.toggle_continuous_training()

Continuous Deployment

Manage model promotion policies between staging and production. Shadow model support for A/B testing.

deployment.set_shadow_model(new_version)

Ready to monitor your models?

Start detecting anomalies, tracking drift, and improving models with production feedback.