To register and deploy a model from the Leaderboard, you must first provide model registration details:

1. On the **Leaderboard**, select the model to use for generating predictions. DataRobot recommends a model with the **Recommended for Deployment** and **Prepared for Deployment** badges. The [model preparation](model-rec-process) process runs feature impact, retrains the model on a reduced feature list, and trains on a higher sample size, followed by the entire sample (latest data for date/time partitioned projects).

    ![](images/prepared-for-deployment.png)

    !!! important
        The **Deploy** tab behaves differently in environments without a dedicated prediction server, as described in the section on [shared modeling workers](deploy-model#use-shared-modeling-workers).

2. Click **Predict > Deploy**. If the Leaderboard model doesn't have the **Prepare for Deployment** badge, DataRobot recommends you click **Prepare for Deployment** to run the [model preparation](model-rec-process#prepare-a-model-for-deployment) process for that model.

    ![](images/prepare-for-deployment-process.png)

    !!! tip
        If you've already added the model to the Model Registry, the registered model version appears in the **Model Versions** list. You can click **Deploy** next to the model and skip the rest of this process.

3. Under **Deploy model**, click **Register to deploy**.

    ![](images/reg-dr-model.png)

4. In the **Register new model** dialog box, provide the following model information:

    ![](images/reg-model-fields.png)

    | Field | Description |
    |-------|-------------|
    | Register model | Select one of the following:<ul><li>**Register new model:** Create a new registered model. This creates the first version (**V1**).</li><li>**Save as a new version to existing model:** Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.</li></ul> |
    | Registered model name / Registered Model | Do one of the following:<ul><li>**Registered model name:** Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, the **Model registration failed** warning appears.</li><li>**Registered Model:** Select the existing registered model you want to add a new version to.</li></ul> |
    | Registered model version | Assigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is always **V1** when you select **Register a new model**. |
    | Prediction threshold | _For binary classification models_. Enter the value a prediction score must exceed to be assigned to the positive class. The default value is `0.5`. For more information, see [Prediction thresholds](#prediction-thresholds). |
    | **Optional settings**  | :~~: | :~~: |
    | Version description | Describe the business problem this model package solves, or, more generally, describe the model represented by this version. |
    | Tags | Click **+ Add item** and enter a **Key** and a **Value** for each key-value pair you want to tag the model _version_ with. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied to **V1**. |
    | Include prediction intervals | _For time series models_, if you enabled the [time series model package prediction intervals](pp-ts-pred-intervals-mlpkg) feature, you can enable the computation of prediction intervals during the time series model package build process. For more information, see [Prediction intervals](#prediction-intervals). |

    ??? note "Binary classification prediction thresholds"
        If you set the <span id="prediction-thresholds">prediction threshold</span> before the [deployment preparation process](model-rec-process), the value does not persist. When deploying the prepared model, if you want it to use a value other than the default, set the value after the model has the **Prepared for Deployment** badge.

    ??? note "Public Preview: Time series prediction intervals"
        If the [time series model package prediction intervals](pp-ts-pred-intervals-mlpkg) feature is enabled, you can access the <span id="prediction-intervals">**Include prediction intervals**</span> setting when you register and deploy a time series model. When you deploy a model package with prediction intervals, the **Predictions > Prediction Intervals** tab is available in the deployment. For deployed model packages built without computing intervals, the deployment's **Predictions > Prediction Intervals** tab is hidden; however, older time series deployments without computed prediction intervals may display the **Prediction Intervals** tab if they were deployed prior to August 2022.

5. Click **Add to registry**. The model opens on the **Model Registry > Registered Models** tab.

6. While the registered model builds, click **Deploy** and then [configure the deployment settings](add-deploy-info).

    ![](images/model-artifact-creation-building.png)

7. Click **Deploy model**.