Nothing close enough? Start from a blank erd → Describe it in one paragraph.
How to use an erd template.
- 01Identify monitored entities
List the core objects you need to track, such as models, datasets, predictions, and ground truth labels.
- 02Define relationships
Connect models to inference logs, features, and alert events so every monitoring query has a clear path.
- 03Add monitoring metrics
Include tables for data drift, model performance, and bias metrics with timestamps and thresholds.
- 04Separate production vs reference data
Model the difference between live prediction data and reference or validation datasets used for comparison.
- 05Generate the ERD
Use the generator to turn your entity list and relationships into a shareable diagram.
Questions about erd templates
What tables are typically included in a machine learning monitoring data model?
Common tables include models, model versions, inference logs, prediction results, ground truth labels, feature snapshots, drift metrics, and alert events.
How do I design an ERD for monitoring model drift?
Create a metrics table linked to model version and timestamp, storing drift measurements for each feature or data distribution.
Should prediction data and ground truth labels be in separate tables?
Yes, keep them separate because ground truth may arrive later and can be used for delayed evaluation; link them via a prediction ID.
Can these ERD examples be adapted for different ML use cases?
Absolutely—replace or extend entities like "recommendation" or "sensor" with your application-specific prediction and feature names while keeping the core monitoring relationships.
Do I need a separate table for bias detection?
Bias detection metrics often belong in a metrics table with a metric type field, but if you track fairness groups and slices, you may need a separate table for group-level metrics.