# Register external models
URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-external-models.html

> Register an external model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages.

# Register external models

To register an external model monitored by the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), add an external model as a registered model or version:

1. On theModel Registry > Registered Modelspage, clickRegister New Model > External model.
2. In theRegister new external modeldialog box, configure the following: FieldDescriptionModel version nameThe name of the model to be registered as a model version.Model version description (optional)Information to describe the model to be added as a registered model version.Model location (optional)The location of the model running outside of DataRobot. Describe the location as a filepath, such as folder1/opt/model.tar.Build EnvironmentThe programming language in which the model was built.Training data (optional)The filename of the training data, uploaded locally or via theAI Catalog. ClickClear selectionto upload and use a different file.Holdout data (optional)The filename of the holdout data, uploaded locally or via theAI Catalog. Use holdout data to set anaccuracy baselineand enable support for target drift and challenger models.TargetThe dataset's column name that the model will predict on.Prediction typeThe type of prediction the model makes. Depending on the prediction type, you must configure additional settings:Regression: No additional settings.Binary: For a binary classification model, enter thePositive classandNegative classlabels and a predictionThreshold.Multiclass: For a multiclass classification model, enter or upload (.csv, .txt) theTarget classesfor your target, one class per line. To ensure that the classes are applied correctly to your model's predictions, the classes should be in the same order as your model's predicted class probabilities.Multilable: For a multilabel model, enter or upload (.csv, .txt) theTarget labelsfor your target, one label per line. To ensure that the labels are applied correctly to your model's predictions, the labels should be in the same order as your model's predicted label probabilities.Text Generation:Premium feature. For more information, seeMonitoring support for generative models.Prediction columnThe column name in the holdout dataset containing the prediction result. If registering atime seriesmodel, select theThis is a time series modelcheckbox and configure the following fields: FieldDescriptionForecast date featureThe column in the training dataset that contains date/time values used by DataRobot to detect the range of dates (the valid forecast range) available for use as the forecast point.Date/time formatThe format used by the date/time features in the training dataset.Forecast point featureThe column in the training dataset that contains the point from which you are making a prediction.Forecast unitThe time unit (seconds, days, months, etc.) that comprise thetime step.Forecast distance featureThe column in the training dataset containing a unique time step—a relative position—within the forecast window. A time series model outputs one row for each forecast distance.Series identifier (optional, used formultiseries models)The column in the training dataset that identifies which series each row belongs to. Finally, configure the registered model settings: 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.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.
3. Once all fields for the external model are defined, clickRegister.

## Set an accuracy baseline

To set an accuracy baseline for external models (which enables target drift and challenger models when deployed), you must provide holdout data. This is because DataRobot cannot use the model to generate predictions that typically serve as a baseline, as the model is hosted in a remote prediction environment outside of the application. Provide holdout data when registering an external model package and specify the column containing predictions.

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