# Monitoring

> Monitoring - Reference public API endpoints for production model performance, drift, and accuracy.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-10-09T14:05:24.770678+00:00` (UTC).

## Primary page

- [Monitoring](https://docs.datarobot.com/en/docs/api/reference/public-api/tag-observability.html.md): Full documentation for this topic (Markdown sidecar).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html.md): Linked from this page.
- [API reference](https://docs.datarobot.com/en/docs/api/reference/index.html.md): Linked from this page.
- [REST API](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html.md): Linked from this page.
- [Monitoring jobs](https://docs.datarobot.com/en/docs/api/reference/public-api/monitoring_jobs.html.md): Linked from this page.
- [Accuracy](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_accuracy.html.md): Linked from this page.
- [Batch monitoring](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_batch_monitoring.html.md): Linked from this page.
- [Bias and fairness](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_bias_fairness.html.md): Linked from this page.
- [Actuals configuration](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_configuration.html.md): Linked from this page.
- [Custom metrics](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_custom_metrics.html.md): Linked from this page.
- [Custom scoring metrics](https://docs.datarobot.com/en/docs/api/reference/public-api/custom_scoring_metrics.html.md): Linked from this page.
- [Data exports](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_data_exploration.html.md): Linked from this page.
- [Data drift](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_drift.html.md): Linked from this page.
- [Service health](https://docs.datarobot.com/en/docs/api/reference/public-api/observability_service_health.html.md): Linked from this page.
- [Tracking agents](https://docs.datarobot.com/en/docs/api/reference/public-api/tracking_agent.html.md): Linked from this page.

## Documentation content

To trust a model to power mission-critical operations, you must have confidence in all aspects of model deployment. By closely tracking the performance of models in production, you can identify potential issues before they impact business operations. Monitoring ranges from whether the service is reliably providing predictions in a timely manner and without errors to ensuring the predictions themselves are reliable.

The predictive performance of a model typically starts to diminish as soon as it’s deployed. For example, someone might be making live predictions on a dataset with customer data, but the customer’s behavioral patterns might have changed due to an economic crisis, market volatility, natural disaster, or even the weather. Models trained on older data that no longer represents the current reality might not just be inaccurate, but irrelevant, leaving the prediction results meaningless or even harmful. Without dedicated production model monitoring, the user cannot know or detect when this happens. If model accuracy starts to decline without detection, the results can impact a business, expose it to risk, and destroy user trust.

The endpoints in this section cover the many aspects of model monitoring.

| Topic | Description |
| --- | --- |
| Monitoring jobs | Define and run batch monitoring jobs against external data sources. |
| Accuracy | Analyze deployment accuracy by comparing predictions to actual outcomes over time. |
| Batch monitoring | Review monitoring statistics grouped by prediction batch instead of time. |
| Bias and fairness | Evaluate fairness metrics and bias across protected features in production. |
| Actuals configuration | Configure actuals ingestion and deployment monitoring settings. |
| Custom metrics | Define, submit, and track custom metrics for deployment performance. |
| Custom scoring metrics | Create and manage custom scoring metrics for model evaluation. |
| Data exports | Export prediction and monitoring data for downstream analysis. |
| Data drift | Monitor data drift and feature distribution changes in production deployments. |
| Service health | Track deployment health metrics such as latency, throughput, and errors. |
| Tracking agents | Install and configure the monitoring agent for external deployment tracking. |