---
title: Deploy registered models
description: Deploy registered model versions from the NextGen Registry.
section_name: NextGen Registry
maturity: open-public-preview

---

# Deploy models from the Registry {: #deploy-models-from-the-registry }

After you register [DataRobot](nxt-register-dr-models) or [external](nxt-register-dr-models) models, you can deploy those models from the Registry by accessing the model version you want to deploy.

To deploy a registered model version from the Registry:

1. On the **Model directory** page, in the table of registered models, click the registered model containing the version you want to deploy, opening the list of versions.

2. In the list of versions, click the version you want to deploy, opening the registered model version panel.

    ![](images/nxt-view-version-info.png)

3. In the upper-right corner of any tab in the registered model version panel, click **Deploy**, and then [configure the deployment settings](#configure-deployment-settings).

    ![](images/nxt-reg-model-ver-deploy.png)

4. After you add the available data and your model is fully defined, click **Deploy model** at the top of the screen.


## Configure deployment settings {: #configure-deployment-settings }

Regardless of where you create a new deployment (a Workbench experiment or the Registry) or the type of artifact (a DataRobot model or an external mode), you are directed to the deployment information page, where you can configure the deployment. The deployment information page outlines the capabilities of your current deployment based on the settings you configure and the data you provide, for example, [training data](glossary/index#training-data), [prediction data](glossary/index#prediction-data), or [actuals](glossary/index#actuals).

### Standard options and information {: standard-options-and-information }

When you initiate model deployment, the **Deployments** tab opens to the **Model Information** and the **Prediction History and Service Health** options:

![](images/nxt-reg-model-deploy-settings.png)

The **Model Information** section provides information about the model being used to make predictions for your deployment. DataRobot uses the files and information from the deployment to complete these fields, so they aren't editable.

   Field    |    Description 
------------|------------------
Model name                  | The name of your model.
Prediction type             | The type of prediction the model is making. For example: Regression, Classification, Multiclass, Anomaly Detection, Clustering, etc.
Threshold                   | The prediction threshold for binary classification models. Records above the threshold are assigned the positive class label and records below the threshold are assigned the negative class label. This field isn't available for Regression or Multiclass models.
Target                      | The dataset column name the model will predict on.
Positive / Negative classes | The positive and negative class values for binary classification models. This field isn't visible for Regression or Multiclass models.
Model Package Id            | The id of the Model Package in the Model Registry.

!!! note
    If you are part of an organization with deployment limits, the **Deployment billing** section notifies you of the number of deployments your organization is using against the [deployment limit](deploy-inventory#live-inventory-updates) and the deployment cost if your organization has exceeded the limit.

The **Prediction History and Service Health** section provides details about your deployment's inference (also known as scoring) data&mdash;the data that contains prediction requests and results from the model.

 Setting                            | Description   
------------------------------------|------------------
Configure prediction environment | Environment where predictions are generated. [Prediction environments](pred-env) allow you to establish access controls and approval workflows.
Configure prediction timestamp   | Determines the method used to time-stamp prediction rows for [Data Drift](data-drift) and [Accuracy](deploy-accuracy) monitoring. <ul><li>**Use time of prediction request**: Use the time you _submitted_ the prediction request to determine the timestamp.</li><li>**Use value from date/time feature**: Use the date/time provided as a feature with the prediction data (e.g., forecast date) to determine the timestamp. Forecast date time-stamping is set automatically for time series deployments. It allows for a common time axis to be used between training data and the basis of data drift and accuracy statistics.</li></ul> This setting doesn't apply to the [Service Health](service-health) prediction timestamp. The Service Health tab _always_ uses the time the prediction server _received_ the prediction request. For more information, see [Time of Prediction](#time-of-prediction) below.<br><br>This setting cannot be changed after the deployment is created and predictions are made.
Set deployment importance        | Determines the importance level of a deployment. These levels&mdash;Critical, High, Moderate, and Low&mdash;determine how a deployment is handled during the [approval process](dep-admin). Importance represents an aggregate of factors relevant to your organization, such as the prediction volume of the deployment, level of exposure, potential financial impact, and more. When a deployment is assigned an importance of Moderate or above, the **Reviewers** notification appears (under [**Model Information**](#model-information)) to inform you that DataRobot will automatically notify users assigned as reviewers whenever the deployment requires review.

{% include 'includes/service-health-prediction-time.md' %}

## Connected deployments {: #connected-deployments }

!!! info "Availability information"
    Connected deployments are off by default. Contact your DataRobot representative or administrator for information on enabling this feature.

    <b>Feature flags:</b> Enable Connecting Deployments

When deploying a custom model created with the **Text Generation** target type, the **Connected deployments** section appears, containing any deployments defined during model creation in the model workshop: 

![](images/nxt-connected-deploy-cus-model.png)

!!! tip "View connected deployments"
    You can :fontawesome-solid-link: **View deployment** in the Console. If you view the deployment in the Console, click the back button to return to the deployment settings page.

These connected deployments report predictions and statistics to the central deployment. To use connected deployments, first, create and deploy audit models to make predictions on an LLM's prompts or outputs; for example, an audit model could identify prompt injection or toxic responses. Then, when you create a custom text generation model, connect it to the deployed audit models by adding one or more `deployment`-type runtime parameters, containing the appropriate deployment IDs, to the `model-metadata.yaml` file. The connected deployment predictions can be combined into the prediction output of the central, text generation model deployment.

For more information, see the [Define connected deployments](nxt-create-custom-model#define-connected-deployments) documentation.

## Advanced options {: #advanced-options }

Click **Show advanced options** to configure the following deployment settings:
 
* [Data Drift](#data-drift)

* [Accuracy](#accuracy)

* [Data Export](#data-export)

* [Challenger Analysis](#challenger-analysis)

* [Advanced Predictions Configuration](#advanced-predictions-configuration) (for [Feature Discovery](fd-overview) deployments)

* [Advanced Service Health Configuration](#advanced-service-health-configuration)

* [Fairness](#fairness)

### Data Drift {: #data-drift }

When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. To enable drift tracking you can configure the following settings:

![](images/nxt-data-drift-settings.png)

 Setting                          | Description     
----------------------------------|------------------
Enable feature drift tracking | Configures DataRobot to track feature drift in a deployment. Training data is required for feature drift tracking.
Enable target monitoring      | Configures DataRobot to track target drift in a deployment. [Actuals](accuracy-settings#add-actuals) are required for target monitoring, and target monitoring is required for [accuracy monitoring](accuracy-settings).
Training data                 | Required to enable feature drift tracking in a deployment.

{% include 'includes/how-dr-tracks-drift-include.md' %}

DataRobot monitors both target and feature drift information by default and displays results in the [Data Drift dashboard](data-drift). Use the **Enable target monitoring** and **Enable feature drift tracking** toggles to turn off tracking if, for example, you have sensitive data that should not be monitored in the deployment.

You can customize how data drift is monitored. See the data drift page for more information on [customizing data drift status](data-drift#customize-data-drift-status) for deployments.

!!! note
    Data drift tracking is only available for deployments using deployment-aware prediction API routes (i.e., `https://example.datarobot.com/predApi/v1.0/deployments/<deploymentId>/predictions`).

### Accuracy {: #accuracy }

Configuring the required settings for the [**Accuracy**](deploy-accuracy) tab allows you to analyze the performance of the model deployment over time using standard statistical measures and exportable visualizations.

![](images/nxt-accuracy-settings.png)

| Setting | Description |
|---------|-------------|
| Association ID | Specifies the column name that contains the association ID in the prediction dataset for your model. Association IDs are required for [setting up accuracy tracking](accuracy-settings#select-an-association-id) in a deployment. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. If your deployment is for a time series project, see [Association IDs for time series deployments](accuracy-settings#association-ids-for-time-series-deployments) to learn how to select or construct an effective association ID. |
| Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. Note that the **Create deployment** button is inactive until you enter an association ID or turn off this toggle. |
| Enable automatic actuals feedback for time series models | _For time series deployments that have indicated an association ID._ Enables the automatic submission of actuals, so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. |

!!! important
     You must set an association ID _before_ making predictions to include those predictions in accuracy tracking. For [agent-monitored](monitoring-agent/index), external model deployments with challengers, the association ID should be `__DataRobot_Internal_Association_ID__` to [report accuracy for the model _and_ its challengers](agent-use#report-accuracy-for-challengers).

### Data Export {: #data-export }

Enable prediction row storage to activate the [**Data Export**](data-export) tab. From there, you can export a deployment's stored training data, prediction data, and actuals to compute and monitor custom business or performance metrics on the [**Custom Metrics**](custom-metrics) tab or outside DataRobot.

![](images/nxt-data-export-settings.png)

Setting                       | Description                
------------------------------|----------------------------
Enable prediction row storage | Enables prediction data storage, a setting required to store and export a deployment's prediction data for use in custom metrics.

### Challenger Analysis {: #challenger-analysis }

DataRobot can securely store prediction request data at the row level for deployments (not supported for external model deployments). This setting must be enabled for any deployment using the [**Challengers**](challengers) tab. In addition to enabling challenger analysis, access to stored prediction request rows enables you to thoroughly audit the predictions and use that data to troubleshoot operational issues. For instance, you can examine the data to understand an anomalous prediction result or why a dataset was malformed.

!!! note
    Contact your DataRobot representative to learn more about data security, privacy, and retention measures or to discuss prediction auditing needs.

![](images/nxt-challenger-settings.png)

 Setting          | Description     
------------------|------------------
Enable prediction rows storage for challenger analysis | Enables the use of challenger models, which allow you to compare models post-deployment and replace the champion model if necessary. Once enabled, prediction requests made for the deployment are collected by DataRobot. Prediction Explanations are not stored.

!!! important
    Prediction requests are only collected if the prediction data is in a valid data format interpretable by DataRobot, such as CSV or JSON. Failed prediction requests with a valid data format are also collected (i.e., missing input features).

### Advanced Predictions Configuration {: #advanced-predictions-configuration }

!!! important
    Feature Discovery is not available in [Workbench experiments](wb-experiment/index); however, you can deploy models created from [Feature Discovery projects in DataRobot Classic](fd-overview) through the NextGen Registry.

[Feature Discovery](fd-overview) identifies and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate multiple datasets into one. This process is based on relationships between datasets and the features within those datasets. DataRobot provides an intuitive relationship editor that allows you to build and visualize these relationships. DataRobot’s Feature Discovery engine analyzes the graphs and the included datasets to determine a feature engineering “recipe” and, from that recipe, generates secondary features for training and predictions. While configuring the deployment settings, you can [select a secondary dataset configuration](fd-predict#select-a-secondary-dataset-configuration).

![](images/nxt-create-secondary-datasets.png)

 Setting          | Description     
------------------|------------------
Secondary datasets configurations | Previews the dataset configuration or provides an option to change it. By default, DataRobot makes predictions using the secondary datasets configuration defined when starting the project. Click **Change** to select an alternative configuration before uploading a new primary dataset.

### Advanced Service Health Configuration {: #advanced-service-health-configuration }

[Segmented Analysis](deploy-segment) identifies operational issues with training and prediction data requests for a deployment. DataRobot enables drill-down analysis of data drift and accuracy statistics by filtering them into unique segment attributes and values.

![](images/nxt-service-health-settings.png)

 Setting          | Description     
------------------|------------------
Track attributes for segmented analysis of training data and predictions | Enables DataRobot to monitor deployment predictions by segments (for example, by categorical features). This setting requires training data and is required to enable Fairness monitoring.

### Fairness {: #fairness }

[Fairness](mlops-fairness) allows you to configure settings for your deployment to identify any biases in the model's predictive behavior. If fairness settings are defined prior to deploying a model, the fields are automatically populated. For additional information, see the section on [defining fairness tests](fairness-metrics#configure-metrics-and-mitigation).

![](images/nxt-fairness-settings.png)

 Setting                 | Description     
-------------------------|------------------
Protected features       | Identifies the dataset columns to measure fairness of model predictions against; must be categorical.
Primary fairness metric  | Defines the statistical measure of parity constraints used to assess fairness.
Favorable target outcome | Defines the outcome value perceived as favorable for the protected class relative to the target.
Fairness threshold       | Defines the fairness threshold to measure if a model performs within appropriate fairness bounds for each protected class.

## Deploy the model {: #deploy-the-model }

After you add the available data and your model is fully defined, click **Deploy model** at the top of the screen.

!!! note
    If the **Deploy model** button is inactive, be sure to either specify an association ID (required for [enabling accuracy monitoring](accuracy-settings)) or toggle off **Require association ID in prediction requests**.
    
The **Creating deployment** message appears, indicating that DataRobot is creating the deployment. After the deployment is created, the **Overview** tab opens.

Click the arrow to the left of the deployment name to return to the deployment inventory.