# Manage Automated Retraining policies

> Manage Automated Retraining policies - Maintain model performance after deployment through Automated
> Retraining.

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 `2026-04-24T16:03:56.573352+00:00` (UTC).

## Primary page

- [Manage Automated Retraining policies](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html): Full documentation for this topic (HTML).

## Sections on this page

- [Manage existing retraining policies](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html#manage-existing-retraining-policies): In-page section heading.
- [View retraining history](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html#view-retraining-history): In-page section heading.

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [MLOps](https://docs.datarobot.com/en/docs/classic-ui/mlops/index.html): Linked from this page.
- [Lifecycle management](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/index.html): Linked from this page.
- [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html): Linked from this page.
- [use the NextGen UI](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html): Linked from this page.

## Documentation content

# Manage Automated Retraining policies

To maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. Upon providing a retraining dataset registered in the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html), you can define up to five retraining policies on each deployment, each consisting of a trigger, a modeling strategy, modeling settings, and a replacement action. When triggered, retraining will produce a new model based on these settings and notify you to consider promoting it.

> [!TIP] Configure retraining settings
> To configure a retraining policy, [use the NextGen UI](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html).

## Manage existing retraining policies

You can start retraining policies or cancel retraining policies from the Classic UI. To edit or delete a retraining policy, [use the NextGen UI](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html).

|  | Element | Definition |
| --- | --- | --- |
| (1) | Manage retraining policies in the NextGen UI | Open the deployment's Mitigation > Retraining tab in the NextGen UI to edit or delete the retraining policy. |
| (2) | Retraining policy row | Click on a retraining policy row to expand it and view the retraining policy runs. |
| (3) | Run | Click the run button to start a policy manually. Alternatively, edit the policy by clicking the policy row and scheduling a run using the retraining trigger. |
| (4) | Cancel | Click the cancel button to cancel a policy that is in progress or scheduled to run. You can't cancel a policy if it has finished successfully, reached the "Creating challenger" or "Replacing model" step, failed, or has already been canceled. |

## View retraining history

You can view all previous runs of a training policy, successful or failed. Each run includes a start time, end time, duration, and—if the run succeeded—links to the resulting project and model package. While only the DataRobot-recommended model for each project is added automatically to the deployment, you may want to explore the project's Leaderboard to find or build alternative models.

> [!NOTE] Note
> Policies cannot be deleted or interrupted while they are running. If the retraining worker and organization have sufficient workers, multiple policies on the same deployment can be running at once.