# 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](/content/demo/index.html) [Documentation](https://documentation.picsellia.com/docs/deployment)

### 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

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

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

```python
# 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.

```python
department.toggle_continuous_training()
```

### Continuous Deployment

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

```python
department.set_shadow_model(new_version)
```

## Ready to monitor your models?

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

[Start Free Trial](/content/trial/index.html) [Request Demo](/content/demo/index.html)