Introduction
Guardian provides real-time monitoring for AI systems in production. Detect drift, anomalies, and performance degradation before they impact users.Create Monitor
Set up monitoring for a model
Log Inference
Log predictions for monitoring
Get Alerts
Retrieve triggered alerts
Key Features
Drift Detection
| Type | Description | Method |
|---|---|---|
| Data Drift | Input feature distribution changes | PSI, KS Test, Chi-Square |
| Prediction Drift | Output distribution changes | KL Divergence, JS Distance |
| Concept Drift | Relationship between inputs and outputs changes | Performance monitoring |
| Label Drift | Ground truth distribution changes | Distribution comparison |
Anomaly Detection
Guardian identifies unusual patterns in:- Input anomalies: Out-of-distribution inputs
- Output anomalies: Unexpected predictions
- Latency anomalies: Performance degradation
- Error spikes: Increased failure rates
Alerting
Configure alerts based on:- Metric thresholds (e.g., PSI > 0.2)
- Percentage changes (e.g., accuracy drops 5%)
- Anomaly detection
- SLA violations
Quick Example
Endpoints
| Method | Endpoint | Description |
|---|---|---|
POST | /guardian/monitors | Create a monitor |
GET | /guardian/monitors/{id} | Get monitor details |
GET | /guardian/monitors | List monitors |
PATCH | /guardian/monitors/{id} | Update monitor config |
DELETE | /guardian/monitors/{id} | Delete a monitor |
POST | /guardian/monitors/{id}/inferences | Log single inference |
POST | /guardian/monitors/{id}/inferences/batch | Log batch of inferences |
GET | /guardian/monitors/{id}/metrics | Get monitoring metrics |
GET | /guardian/monitors/{id}/alerts | Get monitor alerts |
POST | /guardian/alerts/{id}/acknowledge | Acknowledge an alert |
POST | /guardian/alerts/{id}/resolve | Resolve an alert |

