# Deploy external models
URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html

> How to deploy external models by registering and deploying a model package or by uploading training data for the external model directly.

# Deploy external models

To monitor models making predictions on external infrastructure, you can deploy external (remote) models using either of the following methods:

- Deploy an external model package .
- Deploy an external model by uploading historical training data .

After you deploy, you can use the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html) to monitor the external deployment.

## Deploy a registered external model

This section outlines how to deploy a registered external (remote) model. Before proceeding, make sure you have [registered your external model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-external-models.html) in the Model Registry.

> [!NOTE] Important
> To send predictions, first configure the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). Reference the agent's internal documentation for configuration information.

You can deploy a registered external model at any time from the Registered Models page. To do that, you must open a registered model version:

1. On theRegistered Modelspage, click theregistered external modelcontaining the model version you want to deploy.
2. To open the registered model version, do either of the following:
3. In the version header, clickDeploy, and thenconfigure the deployment settings.
4. ClickDeploy model.

## Deploy an external model by uploading training data

This section explains how to upload the training data for a model that made predictions in the past. Uploading the historical predictions directly to the deployment inventory enables you to analyze data drift and accuracy statistics in the past. Instrument the external deployment with the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html) to monitor future predictions and [add additional historical prediction data](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-prediction-data-post-deploy.html) after deployment.

To create a deployment with training data:

1. Navigate toDeploymentsand click the+ Add deploymentlink.
2. Under theAdd a training datasetheader, selectbrowseand selectLocal Fileto upload your XLSX, CSV, or TXT formatted training data. You can also select training data from theAI Catalog.
3. After selecting your training dataset, provide information about the model that used the training data. Once completed, selectContinue to deployment detailsto further configure the deployment.
4. Adddeployment information and complete the deployment.

## Configure an external deployment

After you create an external deployment, there are two options for additional configuration. You can:

- Upload historical prediction datato the deployment to analyze data drift and accuracy in the past.
- Configure the deployment with the monitoring agentusing the monitoring code snippet from thePredictions > Monitoringtabto monitor future predictions.

## Configure prediction data for time series scoring

For time series predictions, if you add prediction data for scoring in the Predictions tab, you must include the following required features in the prediction dataset:

| Feature | Description |
| --- | --- |
| Forecast Distance | Supplied by DataRobot when you download the .mlpkg file. |
| dr_forecast_point | Supplied by DataRobot when you download the .mlpkg file. |
| Datetime_column_name | Defines the date/time feature to use for time-stamping prediction rows. |
| Series_column_name | Defines the feature (series ID) used for multiseries deployments (if applicable). |

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