# Deploy DataRobot models

> Deploy DataRobot models - How to create new deployments from DataRobot AutoML models.

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Companion generated at `2026-04-24T16:03:56.560319+00:00` (UTC).

## Primary page

- [Deploy DataRobot models](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html): Full documentation for this topic (HTML).

## Sections on this page

- [Register and deploy a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#register-and-deploy-a-model): In-page section heading.
- [Deploy a registered model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-a-registered-model): In-page section heading.
- [Use shared modeling workers](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#use-shared-modeling-workers): In-page section heading.

## Related documentation

- [Classic UI documentation](https://docs.datarobot.com/en/docs/classic-ui/index.html): Linked from this page.
- [MLOps](https://docs.datarobot.com/en/docs/classic-ui/mlops/index.html): Linked from this page.
- [Deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html): Linked from this page.
- [Deploy models](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html): Linked from this page.
- [retrain your model](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#retrain-a-model): Linked from this page.
- [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html): Linked from this page.
- [model preparation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html): Linked from this page.
- [time series prediction intervals](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-port-pred-intervals.html): Linked from this page.
- [include pre-computed prediction intervals when registering the model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html): Linked from this page.
- [enabling prediction intervals](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html#set-prediction-intervals-for-time-series-deployments): Linked from this page.
- [configure the deployment settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html): Linked from this page.
- [API key](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html): Linked from this page.

## Documentation content

# 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](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#retrain-a-model) at 100% to improve predictive accuracy. Additionally, DataRobot automatically runs [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) for the model (this also calculates Prediction Explanations, if available).

## Register and deploy a model

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

1. On theLeaderboard, select the model to use for generating predictions. DataRobot recommends a model with theRecommended for DeploymentandPrepared for Deploymentbadges. Themodel preparationprocess 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). ImportantTheDeploytab behaves differently in environments without a dedicated prediction server, as described in the section onshared modeling workers.
2. ClickPredict > Deploy. If the Leaderboard model doesn't have thePrepare for Deploymentbadge, DataRobot recommends you clickPrepare for Deploymentto run themodel preparationprocess for that model. TipIf you've already added the model to the Model Registry, the registered model version appears in theModel Versionslist. You can clickDeploynext to the model and skip the rest of this process.
3. UnderDeploy model, clickRegister to deploy.
4. In theRegister new modeldialog box, provide the following model information: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).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.Registered model name / Registered ModelDo one of the following: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, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Prediction thresholdFor binary classification models. Enter the value a prediction score must exceed to be assigned to the positive class. The default value is0.5. For more information, seePrediction thresholds.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1.Include prediction intervalsFor time series models. Enable the computation of a model'stime series prediction intervals(from 1 to 100). Time series prediction intervals may take a long time to compute, depending on the number of series in the dataset, the number of features, the blueprint, etc. Consider if intervals are required in your deployment before enabling this setting. For more information see, theprediction intervals consideration. Prediction intervals in DataRobot serverless prediction environmentsIn a DataRobot serverless prediction environment, to make predictions with time-series prediction intervals included,you mustinclude pre-computed prediction intervals when registering the model package. If you don't pre-compute prediction intervals, the deployment resulting from the registered model doesn't supportenabling prediction intervals. Binary classification prediction thresholdsIf you set theprediction thresholdbefore thedeployment preparation 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 thePrepared for Deploymentbadge. Time series prediction intervals considerationWhen you deploy atime series model package with prediction intervals, thePredictions > Prediction Intervalstab is available in the deployment. For deployed model packages built without computing intervals, the deployment'sPredictions > Prediction Intervalstab is hidden; however, older time series deployments without computed prediction intervals may display thePrediction Intervalstab if they were deployed prior to August 2022.
5. ClickAdd to registry. The model opens on theModel Registry > Registered Modelstab.
6. While the registered model builds, clickDeployand thenconfigure the deployment settings.
7. ClickDeploy 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 theRegistered Modelspage, click the registered model containing 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.

## 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 theLeaderboard, select the model to use for generating predictions, and then clickPredict > Deploy Model API.
2. ClickShow Exampleto generate and display a usage example and define the following: FieldDescription1API_TOKENTheAPI key.2PROJECT_ID/MODEL_IDThe project and model IDs, available in the sample.3dr.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.