# Register DataRobot models
URL: https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html

> Add a DataRobot model to the NextGen Registry or register a model from a Workbench experiment.

# Register DataRobot models

After you create an [experiment](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html) and train models, you can register one or more models from Workbench or directly in Registry. If a registered model already exists to solve a specific business problem, you can add new models solving the same problem as registered model versions, providing a model management experience organized by problem type.

## Register a model from Workbench

To register a model from a Workbench experiment:

1. In aWorkbenchexperiment, select the model from theModelslist and then clickModel actions>Register model:
2. In theRegister a modelpanel, underConfigure the model, select one of the following: Then, configure the following fields: FieldDescriptionRegistered model name / Registered modelDo one of the following:Registered model name:When registering a new model, enter a descriptive name for the new registered model. Note that the name does not need to be unique.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name, date, and time. Change or modify the name as necessary.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 as 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. If necessary, you can configure the followingOptional settings: FieldDescriptionRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1.Include prediction intervalsFor time series models. Enable the computation of a model's time 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. 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. Returning to WorkbenchIf you clickCloseon this page to return to Workbench, you lose the configuration progress on this page.
3. ClickRegister model. The model version opens on theRegistry > Modelspage with aBuildingstatus. You candeploy the modelat any time.

## Register a model from Registry

To register a model from Registry:

1. In theRegistry, on theModelstab, click+ Register a model(or thebutton when the registered model or version info panel is open): TheRegister a modelpanel opens to theExternal modeltab.
2. Click theDataRobot modeltab and then, underConfigure the model, select one of the following options: Then, configure the following fields: FieldDescriptionUse CaseSelect the Use Case in Workbench containing the model you want to register.ExperimentSelect the experiment in Workbench containing the model you want to register.DataRobot ModelSelect the model you want to register.Registered model name / Registered modelDo one of the following:Registered model name:When registering a new model, enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, a warning appears.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name, date, and time. Change or modify the name as necessary.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 as 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.Optional settingsRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1.Include prediction intervalsFor time series models. Enable the computation of a model's time 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. 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. Returning to WorkbenchIf you clickCancelon this page to return to theRegistry, you lose the configuration progress on this page.
3. ClickRegister Model. The model version opens on theRegistry > Modelspage with aBuildingstatus. You candeploy the modelat any time.

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