Reviewing prediction errors with the Observability Dashboard
The Observability Dashboard offers a unified view and actionable insights for errors detected in Predictive Intelligence. Use this dashboard to visualize logged errors and gain information on prediction reliability and potential problem areas.
View the PI - Observability Dashboard by navigating to . The dashboard contains the following widgets.
- Total Number of Prediction Errors
- Prediction Errors Breakdown by Date
- Prediction Errors Count by Capability
- Prediction Error Count by Error Type
- Error Types by Capability
- Successful and Unsuccessful Predictions Breakdown by Date
The PI - Observability Dashboard draws from a table dedicated to logging prediction errors: ML Predictor Error Logs [ml_predictor_error_logs].
- Fields in the table include Error Type, Error Message, Status code, Solution, and Capability.
- View this table's records directly by navigating to .
- Roles required to access the table: ml_report_user or ml_admin.
The table logs the following types of granular errors.
- Client-side issues (400 Series) — captures request errors such as timeouts and invalid inputs.
- Server-side issues (500 Series) — tracks internal server errors encountered during prediction.
- Internal prediction failures — identifies instances when the model was unable to generate a prediction.
- Low confidence predictions — records log results falling below a defined confidence threshold.
주:
Errors in training aren't included in this table. For a dashboard reporting on training errors, see Predictive Intelligence Usage Analytics dashboard.