---
title: Deploy DataRobot models
description: How to create new deployments from DataRobot AutoML models.

---

# Deploy DataRobot models {: #deploy-datarobot-models }

You can register and deploy models you build with DataRobot AutoML using the Model Registry. In most cases, before deployment, you should unlock holdout and [retrain your model](creating-addl-models#retrain-a-model) at 100% to improve predictive accuracy. Additionally, DataRobot automatically runs [**Feature Impact**](feature-impact) for the model (this also calculates **Prediction Explanations**, if available).

## Register and deploy a model {: #register-and-deploy-a-model }

{% include 'includes/deploy-leaderboard.md' %}

## Deploy a registered model {: #deploy-a-registered-model }

In the Model Registry, you can deploy a registered model at any time from the **Registered Models** page. To do that, you must open a registered model version:

1. On the **Registered Models** page, click the registered model containing the model version you want to deploy.

2. To open the registered model version, do either of the following:

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

    * To open the version in the current tab, click the row for the version you want to access.
    
    * To open the version in a new tab, click the open icon (![](images/icon-open.png)) next to the **Type** column for the version you want to access.

3. In the version header, click **Deploy**, and then [configure the deployment settings](add-deploy-info).

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

##  Use shared modeling workers {: #use-shared-modeling-workers }

If you do not have a dedicated prediction server instance, you can use a node that shares workers with your model-building activities. 

In this scenario, the deployment workflow has a different interface:

1. From the **Leaderboard**, select the model to use for generating predictions, and then click **Predict > Deploy Model API**.

    ![](images/deploy-5.png)

2. Click **Show Example** to generate and display a usage example and define the following:

    ![](images/deploy-6.png)

    | | Field | Description |
    |-|-------|-------------|
    | ![](images/icon-1.png) | `API_TOKEN`             | The [API key](api-key-mgmt). |
    | ![](images/icon-2.png) | `PROJECT_ID`/`MODEL_ID` | The project and model IDs, available in the sample. |
    | ![](images/icon-3.png) | `dr.Client(endpoint='https://app.datarobot.com/api/v2', token=API_TOKEN)` | The shared instance endpoint, available in the sample. The DataRobot Python client uses the API key you set for authentication so no key or username is required. |

3. To execute the file, follow the instructions in the comments included in the example snippet.
