Monitor Models
In Production
Real-time observability for computer vision models. Detect anomalies, track drift, and close the feedback loop automatically.
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
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
Last 24 hours
Avg Latency
P50 response time
Accuracy
Based on reviewed predictions
# 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)Track distribution changes
Compare production data against your training baseline. Get alerted when distributions shift beyond acceptable thresholds.
Close the feedback loop
Convert production failures into training data. Review predictions, attach to datasets, and trigger retraining automatically.
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.