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On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

Overview tab

When you select a deployment from the Deployments page (also called the deployment inventory), DataRobot opens to the Overview page for that deployment.

The Overview page provides a model- and environment-specific summary that describes the deployment, including the information you supplied when creating the deployment and any model replacement activity.


The Summary section of the Overview tab Lists user-provided information about deployments, including:

  • Name
  • Description
  • Prediction Environment
  • Importance

Where applicable, click the pencil icon () to edit this information; changes affect the Deployments page.


The Content section of the Overview tab lists a deployment's model and environment-specific information, including:

Field Description
Dataset Filename of the dataset used to create the deployment's current model.
Target Feature name of the target used by the deployment's current model.
Model Model name of the deployment's current model. Click to open the Leaderboard to the model blueprint (Describe > Blueprint) for the model.
Model ID Model ID number of the deployment's current model. Click to copy the number to your clipboard. In addition, you can copy the Model ID of any models deployed in the past from the deployment logs (History > Logs).
Deployment ID Deployment ID number of the current deployment. Click to copy the number to your clipboard.
Build Environment Build environment used by the deployment's current model (e.g., DataRobot, Python, R, or Java).
Project Project data used for the currently deployed model. Click to open the Data > Project Data tab for the project providing data to the deployed model.


The information included in this list differs for deployments using custom models and external environments. It can also include information dependent on the target type.


Tracking deployment events in a deployment's History section is essential when a deployed model supports a critical use case. You can maintain deployment stability by monitoring the Governance and Logs events. These events include when the model was deployed or replaced. The deployment history links these events to the user responsible for the change.


Many organizations, especially those in highly regulated industries, need greater control over model deployment and management. Administrators can define deployment approval policies to facilitate this enhanced control. However, by default, there aren't any approval requirements before deploying.

You can find a deployment's available governance log details under History > Governance, including an audit trail for any deployment approval policies triggered for the deployment.


When a model begins to experience data or accuracy drift, you should gather a new dataset, train a new model, and replace the old model. The details of this deployment lifecycle are recorded, including timestamps for model creation and deployment and a record of the user responsible for the recorded action. Any user with deployment owner permissions can replace the deployed model.

You can find a deployment's model-related events under History > Logs, including the creation and deployment dates and any model replacement events. Each model replacement event reports the replacement date and justification (if provided). In addition, you can find and copy the Model ID of any previously deployed model.

Deployment Reports

Monitoring reports are a critical part of the deployment governance process. DataRobot allows you to download deployment reports, compiling deployment status, charts, and overall quality into a sharable report. Deployment reports are compatible with all deployment types.

For more information, see Deployment reports.

Updated May 31, 2024