Models
Use the endpoints described below to create and manage DataRobot models.
Create a model package
Operation path: POST /api/v2/modelPackages/fromJSON/
Authentication requirements: BearerAuth
Create a model package from json.
Body parameter
{
"properties": {
"datasets": {
"description": "The dataset information for the model package.",
"properties": {
"holdoutDataCatalogId": {
"description": "The ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "The version ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogId": {
"description": "The ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "The version ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.37"
},
"geospatialMonitoring": {
"description": "Geospatial monitoring information for the model package",
"properties": {
"primaryLocationColumn": {
"description": "The name of the geo-analysis column,",
"type": [
"string",
"null"
]
}
},
"required": [
"primaryLocationColumn"
],
"type": "object",
"x-versionadded": "v2.37"
},
"modelDescription": {
"description": "The model description information for the model package.",
"properties": {
"buildEnvironmentType": {
"description": "The build environment type of the model.",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Julia",
"Legacy",
"Other"
],
"type": "string"
},
"description": {
"description": "A description of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"location": {
"description": "The location of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"modelName": {
"description": "The model name.",
"maxLength": 512,
"type": "string"
}
},
"type": "object",
"x-versionadded": "v2.37"
},
"modelId": {
"description": "The ID of the model.",
"type": "string"
},
"name": {
"description": "The model package name.",
"maxLength": 1024,
"type": "string"
},
"registeredModelName": {
"description": "The registered model name.",
"maxLength": 1024,
"type": "string",
"x-versionadded": "v2.39"
},
"target": {
"description": "The target information for the model package.",
"properties": {
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.",
"items": {
"maxLength": 128,
"type": "string"
},
"maxItems": 1000,
"type": "array"
},
"name": {
"description": "name of the target column",
"maxLength": 128,
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"maxLength": 128,
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"name",
"type"
],
"type": "object",
"x-versionadded": "v2.37"
},
"textGeneration": {
"description": "Text generation information for the model package",
"properties": {
"prompt": {
"description": "Name of the prompt column",
"type": [
"string",
"null"
]
}
},
"required": [
"prompt"
],
"type": "object",
"x-versionadded": "v2.37"
},
"timeseries": {
"description": "Time series information for the model package.",
"properties": {
"datetimeColumnFormat": {
"description": "The date format for the forecast date and forecast point column.",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "The name of the forecast date column.",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"effectiveFeatureDerivationWindowStart": {
"description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"featureDerivationWindowEnd": {
"description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "The name of the forecast distance column.",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "A list of integer forecast distances.",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances.",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": "string"
},
"forecastPointColumnName": {
"description": "The name of the forecast point column.",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"default": false,
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
]
},
"isTraditionalTimeSeries": {
"default": false,
"description": "Determines if the model is a traditional time series model.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "The name of the series column in the case of a multi-series date.",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"forecastDistanceColumnName",
"forecastDistancesTimeUnit",
"forecastPointColumnName"
],
"type": "object",
"x-versionadded": "v2.37"
}
},
"required": [
"name",
"target"
],
"type": "object",
"x-versionadded": "v2.37"
}
Parameters
Example responses
200 Response
{
"properties": {
"activeDeploymentCount": {
"description": "Number of deployments currently using this model package",
"type": "integer"
},
"buildStatus": {
"description": "Model package build status",
"enum": [
"inProgress",
"complete",
"failed"
],
"type": [
"string",
"null"
]
},
"capabilities": {
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
},
"datasets": {
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
},
"id": {
"description": "ID of the Model package",
"type": "string"
},
"importMeta": {
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
},
"isArchived": {
"description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)",
"type": "boolean"
},
"isDeprecated": {
"description": "Whether the model package is deprecated. eg. python2 models are deprecated.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"mlpkgFileContents": {
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
},
"modelDescription": {
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
},
"modelExecutionType": {
"description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not",
"enum": [
"dedicated",
"custom_inference_model",
"external"
],
"type": "string"
},
"modelId": {
"description": "ID of the model",
"type": "string"
},
"modelKind": {
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
},
"name": {
"description": "Model package name",
"type": "string"
},
"permissions": {
"description": "List of action permissions the user making the request has on the model package",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.20"
},
"sourceMeta": {
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
},
"target": {
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
},
"timeseries": {
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
},
"updatedBy": {
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
},
"userProvidedId": {
"description": "A user-provided unique ID associated with the given custom inference model.",
"type": "string"
}
},
"required": [
"activeDeploymentCount",
"capabilities",
"datasets",
"id",
"importMeta",
"isArchived",
"isDeprecated",
"modelDescription",
"modelExecutionType",
"modelId",
"modelKind",
"name",
"permissions",
"sourceMeta",
"target",
"timeseries",
"updatedBy"
],
"type": "object"
}
Responses
Retrieve an archive (tar by project ID
Operation path: GET /api/v2/projects/{projectId}/models/{modelId}/logs/
Authentication requirements: BearerAuth
Retrieve an archive (tar.gz) of the logs produced and persisted by a model. Note that only blueprints with custom tasks create persistent logs - this will not work with any other type of model.
Parameters
| Name |
In |
Type |
Required |
Description |
| projectId |
path |
string |
true |
The project ID |
| modelId |
path |
string |
true |
The model ID |
Example responses
200 Response
{
"properties": {
"data": {
"description": "An archive (tar.gz) of the logs produced and persisted by a model.",
"format": "binary",
"type": "string"
}
},
"required": [
"data"
],
"type": "object"
}
Responses
Retrieve training artifact by id by project ID
Operation path: GET /api/v2/projects/{projectId}/models/{modelId}/trainingArtifact/
Authentication requirements: BearerAuth
Retrieve an archive (tar.gz) of the artifacts produced and persisted by a model. Note that only blueprints with custom tasks create these artifacts - this will not work with any other type of model.
Parameters
| Name |
In |
Type |
Required |
Description |
| projectId |
path |
string |
true |
The project ID |
| modelId |
path |
string |
true |
The model ID |
Example responses
200 Response
{
"properties": {
"data": {
"description": "An archive (tar.gz) of the artifacts produced and persisted by a model.",
"format": "binary",
"type": "string"
}
},
"required": [
"data"
],
"type": "object"
}
Responses
Schemas
{
"properties": {
"data": {
"description": "An archive (tar.gz) of the artifacts produced and persisted by a model.",
"format": "binary",
"type": "string"
}
},
"required": [
"data"
],
"type": "object"
}
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| data |
string(binary) |
true |
|
An archive (tar.gz) of the artifacts produced and persisted by a model. |
CustomModelDetails
{
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
Details of the custom model associated to this registered model version
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| createdAt |
string |
true |
|
Time when the custom model was created |
| creatorEmail |
string,null |
false |
|
Email of the user who created the custom model |
| creatorId |
string |
true |
|
ID of the creator of the custom model |
| creatorName |
string,null |
false |
|
Name of the user who created the custom model |
| id |
string |
true |
|
ID of the associated custom model |
| versionLabel |
string,null |
false |
|
Label of associated custom model version. |
MlpkgFileContents
{
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
}
Information about the content of .mlpkg artifact
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| allTimeSeriesPredictionIntervals |
boolean,null |
false |
|
Whether .mlpkg contains TS prediction intervals computed for all percentiles |
ModelPackageCapabilities
{
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
}
Capabilities of the current model package.
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| supportsAutomaticActuals |
boolean |
false |
|
Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package. |
| supportsChallengerModels |
boolean |
true |
|
Whether Challenger Models are supported by this model package. |
| supportsFeatureDriftTracking |
boolean |
true |
|
Whether Feature Drift is supported by this model package. |
| supportsHumilityRecommendedRules |
boolean |
true |
|
Whether calculating values for recommended Humility Rules is supported by this model package. |
| supportsHumilityRules |
boolean |
true |
|
Whether Humility Rules are supported by this model package. |
| supportsHumilityRulesDefaultCalculations |
boolean |
true |
|
Whether calculating default values for Humility Rules is supported by this model package. |
| supportsPredictionWarning |
boolean |
true |
|
Whether Prediction Warnings are supported by this model package. |
| supportsRetraining |
boolean |
false |
|
Whether deployment supports retraining. |
| supportsScoringCodeDownload |
boolean |
false |
|
Whether scoring code download is supported by this model package. |
| supportsSecondaryDatasets |
boolean |
true |
|
If the deployments supports secondary datasets. |
| supportsSegmentedAnalysisDriftAndAccuracy |
boolean |
true |
|
Whether tracking features in training and predictions data for segmented analysis is supported by this model package. |
| supportsShapBasedPredictionExplanations |
boolean |
true |
|
Whether shap-based prediction explanations are supported by this model package. |
| supportsTargetDriftTracking |
boolean |
true |
|
Whether Target Drift is supported by this model package. |
ModelPackageCreateExternal
{
"properties": {
"datasets": {
"description": "The dataset information for the model package.",
"properties": {
"holdoutDataCatalogId": {
"description": "The ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "The version ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogId": {
"description": "The ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "The version ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.37"
},
"geospatialMonitoring": {
"description": "Geospatial monitoring information for the model package",
"properties": {
"primaryLocationColumn": {
"description": "The name of the geo-analysis column,",
"type": [
"string",
"null"
]
}
},
"required": [
"primaryLocationColumn"
],
"type": "object",
"x-versionadded": "v2.37"
},
"modelDescription": {
"description": "The model description information for the model package.",
"properties": {
"buildEnvironmentType": {
"description": "The build environment type of the model.",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Julia",
"Legacy",
"Other"
],
"type": "string"
},
"description": {
"description": "A description of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"location": {
"description": "The location of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"modelName": {
"description": "The model name.",
"maxLength": 512,
"type": "string"
}
},
"type": "object",
"x-versionadded": "v2.37"
},
"modelId": {
"description": "The ID of the model.",
"type": "string"
},
"name": {
"description": "The model package name.",
"maxLength": 1024,
"type": "string"
},
"registeredModelName": {
"description": "The registered model name.",
"maxLength": 1024,
"type": "string",
"x-versionadded": "v2.39"
},
"target": {
"description": "The target information for the model package.",
"properties": {
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.",
"items": {
"maxLength": 128,
"type": "string"
},
"maxItems": 1000,
"type": "array"
},
"name": {
"description": "name of the target column",
"maxLength": 128,
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"maxLength": 128,
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"name",
"type"
],
"type": "object",
"x-versionadded": "v2.37"
},
"textGeneration": {
"description": "Text generation information for the model package",
"properties": {
"prompt": {
"description": "Name of the prompt column",
"type": [
"string",
"null"
]
}
},
"required": [
"prompt"
],
"type": "object",
"x-versionadded": "v2.37"
},
"timeseries": {
"description": "Time series information for the model package.",
"properties": {
"datetimeColumnFormat": {
"description": "The date format for the forecast date and forecast point column.",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "The name of the forecast date column.",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"effectiveFeatureDerivationWindowStart": {
"description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"featureDerivationWindowEnd": {
"description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "The name of the forecast distance column.",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "A list of integer forecast distances.",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances.",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": "string"
},
"forecastPointColumnName": {
"description": "The name of the forecast point column.",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"default": false,
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
]
},
"isTraditionalTimeSeries": {
"default": false,
"description": "Determines if the model is a traditional time series model.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "The name of the series column in the case of a multi-series date.",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"forecastDistanceColumnName",
"forecastDistancesTimeUnit",
"forecastPointColumnName"
],
"type": "object",
"x-versionadded": "v2.37"
}
},
"required": [
"name",
"target"
],
"type": "object",
"x-versionadded": "v2.37"
}
Properties
ModelPackageDatasets
{
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
}
dataset information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| baselineSegmentedBy |
[string] |
true |
|
Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value. |
| datasetName |
string,null |
true |
|
Name of dataset used to train the model |
| holdoutDataCatalogId |
string,null |
true |
|
ID for holdout data (returned from uploading a data set) |
| holdoutDataCatalogVersionId |
string,null |
true |
|
Version ID for holdout data (returned from uploading a data set) |
| holdoutDataCreatedAt |
string,null |
false |
|
Time when the holdout data item was created |
| holdoutDataCreatorEmail |
string,null |
false |
|
Email of the user who created the holdout data item |
| holdoutDataCreatorId |
string,null |
false |
|
ID of the creator of the holdout data item |
| holdoutDataCreatorName |
string,null |
false |
|
Name of the user who created the holdout data item |
| holdoutDatasetName |
string,null |
true |
|
Name of dataset used for model holdout |
| targetHistogramBaseline |
string |
false |
|
Values used to establish the training baseline |
| trainingDataCatalogId |
string,null |
true |
|
ID for training data (returned from uploading a data set) |
| trainingDataCatalogVersionId |
string,null |
true |
|
Version ID for training data (returned from uploading a data set) |
| trainingDataCreatedAt |
string,null |
false |
|
Time when the training data item was created |
| trainingDataCreatorEmail |
string,null |
false |
|
Email of the user who created the training data item |
| trainingDataCreatorId |
string,null |
false |
|
ID of the creator of the training data item |
| trainingDataCreatorName |
string,null |
false |
|
Name of the user who created the training data item |
| trainingDataSize |
integer |
false |
|
Number of rows in training data (used by DR models) |
Enumerated Values
| Property |
Value |
| targetHistogramBaseline |
[predictions, actuals] |
ModelPackageDatasetsCreate
{
"description": "The dataset information for the model package.",
"properties": {
"holdoutDataCatalogId": {
"description": "The ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "The version ID for Holdout data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogId": {
"description": "The ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "The version ID for training data (returned from uploading a dataset).",
"type": [
"string",
"null"
]
}
},
"type": "object",
"x-versionadded": "v2.37"
}
The dataset information for the model package.
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| holdoutDataCatalogId |
string,null |
false |
|
The ID for Holdout data (returned from uploading a dataset). |
| holdoutDataCatalogVersionId |
string,null |
false |
|
The version ID for Holdout data (returned from uploading a dataset). |
| trainingDataCatalogId |
string,null |
false |
|
The ID for training data (returned from uploading a dataset). |
| trainingDataCatalogVersionId |
string,null |
false |
|
The version ID for training data (returned from uploading a dataset). |
ModelPackageExternalGeospatialMonitoring
{
"description": "Geospatial monitoring information for the model package",
"properties": {
"primaryLocationColumn": {
"description": "The name of the geo-analysis column,",
"type": [
"string",
"null"
]
}
},
"required": [
"primaryLocationColumn"
],
"type": "object",
"x-versionadded": "v2.37"
}
Geospatial monitoring information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| primaryLocationColumn |
string,null |
true |
|
The name of the geo-analysis column, |
{
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
}
Information from when this Model Package was first saved
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| containsFearPipeline |
boolean,null |
false |
|
Exists for imported models only, indicates thatmodel package contains file with fear pipeline. |
| containsFeaturelists |
boolean,null |
false |
|
Exists for imported models only, indicates thatmodel package contains file with featurelists. |
| containsLeaderboardMeta |
boolean,null |
false |
|
Exists for imported models only, indicates thatmodel package contains file with leaderboard meta. |
| containsProjectMeta |
boolean,null |
false |
|
Exists for imported models only, indicates thatmodel package contains file with project meta. |
| creatorFullName |
string,null |
true |
|
Full name of the person who created this model package |
| creatorId |
string |
true |
|
User ID of the person who created this Model Package |
| creatorUsername |
string |
true |
|
Username of the person who created this model package |
| dateCreated |
string |
true |
|
When this Model Package was created |
| originalFileName |
string,null |
true |
|
Exists for imported models only, the original file name that was uploaded |
ModelPackageModelDescription
{
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
}
model description information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| buildEnvironmentType |
string |
true |
|
build environment type of the model |
| description |
string,null |
true |
|
a description of the model |
| location |
string,null |
true |
|
location of the model |
| modelCreatedAt |
string,null |
false |
|
time when the model was created |
| modelCreatorEmail |
string,null |
false |
|
email of the user who created the model |
| modelCreatorId |
string,null |
false |
|
ID of the creator of the model |
| modelCreatorName |
string,null |
false |
|
name of the user who created the model |
| modelName |
string |
false |
|
model name |
Enumerated Values
| Property |
Value |
| buildEnvironmentType |
[DataRobot, Python, R, Java, Other] |
ModelPackageModelDescriptionCreate
{
"description": "The model description information for the model package.",
"properties": {
"buildEnvironmentType": {
"description": "The build environment type of the model.",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Julia",
"Legacy",
"Other"
],
"type": "string"
},
"description": {
"description": "A description of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"location": {
"description": "The location of the model.",
"maxLength": 2048,
"type": [
"string",
"null"
]
},
"modelName": {
"description": "The model name.",
"maxLength": 512,
"type": "string"
}
},
"type": "object",
"x-versionadded": "v2.37"
}
The model description information for the model package.
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| buildEnvironmentType |
string |
false |
|
The build environment type of the model. |
| description |
string,null |
false |
maxLength: 2048
|
A description of the model. |
| location |
string,null |
false |
maxLength: 2048
|
The location of the model. |
| modelName |
string |
false |
maxLength: 512
|
The model name. |
Enumerated Values
| Property |
Value |
| buildEnvironmentType |
[DataRobot, Python, R, Java, Julia, Legacy, Other] |
ModelPackageModelKind
{
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
}
Model attribute information
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| isAnomalyDetectionModel |
boolean |
true |
|
true if this is an anomaly detection model |
| isCombinedModel |
boolean |
true |
|
true if model is a combined model |
| isFeatureDiscovery |
boolean |
true |
|
true if this model uses the Feature Discovery feature |
| isMultiseries |
boolean |
true |
|
true if model is multiseries |
| isTimeSeries |
boolean |
true |
|
true if model is time series |
| isUnsupervisedLearning |
boolean |
true |
|
true if model used unsupervised learning |
ModelPackageRetrieveResponse
{
"properties": {
"activeDeploymentCount": {
"description": "Number of deployments currently using this model package",
"type": "integer"
},
"buildStatus": {
"description": "Model package build status",
"enum": [
"inProgress",
"complete",
"failed"
],
"type": [
"string",
"null"
]
},
"capabilities": {
"description": "Capabilities of the current model package.",
"properties": {
"supportsAutomaticActuals": {
"description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsChallengerModels": {
"description": "Whether Challenger Models are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsFeatureDriftTracking": {
"description": "Whether Feature Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRecommendedRules": {
"description": "Whether calculating values for recommended Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRules": {
"description": "Whether Humility Rules are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsHumilityRulesDefaultCalculations": {
"description": "Whether calculating default values for Humility Rules is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2"
},
"supportsPredictionWarning": {
"description": "Whether Prediction Warnings are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsRetraining": {
"description": "Whether deployment supports retraining.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsScoringCodeDownload": {
"description": "Whether scoring code download is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsSecondaryDatasets": {
"description": "If the deployments supports secondary datasets.",
"type": "boolean",
"x-versionadded": "v2.28",
"x-versiondeprecated": "v2.29"
},
"supportsSegmentedAnalysisDriftAndAccuracy": {
"description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsShapBasedPredictionExplanations": {
"description": "Whether shap-based prediction explanations are supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
},
"supportsTargetDriftTracking": {
"description": "Whether Target Drift is supported by this model package.",
"type": "boolean",
"x-versionadded": "v2.25.2",
"x-versiondeprecated": "v2.29"
}
},
"required": [
"supportsChallengerModels",
"supportsFeatureDriftTracking",
"supportsHumilityRecommendedRules",
"supportsHumilityRules",
"supportsHumilityRulesDefaultCalculations",
"supportsPredictionWarning",
"supportsSecondaryDatasets",
"supportsSegmentedAnalysisDriftAndAccuracy",
"supportsShapBasedPredictionExplanations",
"supportsTargetDriftTracking"
],
"type": "object"
},
"datasets": {
"description": "dataset information for the model package",
"properties": {
"baselineSegmentedBy": {
"description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.",
"items": {
"type": "string"
},
"type": "array"
},
"datasetName": {
"description": "Name of dataset used to train the model",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogId": {
"description": "ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCatalogVersionId": {
"description": "Version ID for holdout data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"holdoutDataCreatedAt": {
"description": "Time when the holdout data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorEmail": {
"description": "Email of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorId": {
"default": null,
"description": "ID of the creator of the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDataCreatorName": {
"description": "Name of the user who created the holdout data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"holdoutDatasetName": {
"description": "Name of dataset used for model holdout",
"type": [
"string",
"null"
]
},
"targetHistogramBaseline": {
"description": "Values used to establish the training baseline",
"enum": [
"predictions",
"actuals"
],
"type": "string"
},
"trainingDataCatalogId": {
"description": "ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCatalogVersionId": {
"description": "Version ID for training data (returned from uploading a data set)",
"type": [
"string",
"null"
]
},
"trainingDataCreatedAt": {
"description": "Time when the training data item was created",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorEmail": {
"description": "Email of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorId": {
"default": null,
"description": "ID of the creator of the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataCreatorName": {
"description": "Name of the user who created the training data item",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
},
"trainingDataSize": {
"description": "Number of rows in training data (used by DR models)",
"type": "integer"
}
},
"required": [
"baselineSegmentedBy",
"datasetName",
"holdoutDataCatalogId",
"holdoutDataCatalogVersionId",
"holdoutDatasetName",
"trainingDataCatalogId",
"trainingDataCatalogVersionId"
],
"type": "object"
},
"id": {
"description": "ID of the Model package",
"type": "string"
},
"importMeta": {
"description": "Information from when this Model Package was first saved",
"properties": {
"containsFearPipeline": {
"description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.",
"type": [
"boolean",
"null"
]
},
"containsFeaturelists": {
"description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.",
"type": [
"boolean",
"null"
]
},
"containsLeaderboardMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.",
"type": [
"boolean",
"null"
]
},
"containsProjectMeta": {
"description": "Exists for imported models only, indicates thatmodel package contains file with project meta.",
"type": [
"boolean",
"null"
]
},
"creatorFullName": {
"description": "Full name of the person who created this model package",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "User ID of the person who created this Model Package",
"type": "string"
},
"creatorUsername": {
"description": "Username of the person who created this model package",
"type": "string"
},
"dateCreated": {
"description": "When this Model Package was created",
"type": "string"
},
"originalFileName": {
"description": "Exists for imported models only, the original file name that was uploaded",
"type": [
"string",
"null"
]
}
},
"required": [
"creatorFullName",
"creatorId",
"creatorUsername",
"dateCreated",
"originalFileName"
],
"type": "object"
},
"isArchived": {
"description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)",
"type": "boolean"
},
"isDeprecated": {
"description": "Whether the model package is deprecated. eg. python2 models are deprecated.",
"type": "boolean",
"x-versionadded": "v2.29"
},
"mlpkgFileContents": {
"description": "Information about the content of .mlpkg artifact",
"properties": {
"allTimeSeriesPredictionIntervals": {
"description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.31"
}
},
"type": "object"
},
"modelDescription": {
"description": "model description information for the model package",
"properties": {
"buildEnvironmentType": {
"description": "build environment type of the model",
"enum": [
"DataRobot",
"Python",
"R",
"Java",
"Other"
],
"type": "string"
},
"description": {
"description": "a description of the model",
"type": [
"string",
"null"
]
},
"location": {
"description": "location of the model",
"type": [
"string",
"null"
]
},
"modelCreatedAt": {
"description": "time when the model was created",
"type": [
"string",
"null"
]
},
"modelCreatorEmail": {
"description": "email of the user who created the model",
"type": [
"string",
"null"
]
},
"modelCreatorId": {
"default": null,
"description": "ID of the creator of the model",
"type": [
"string",
"null"
]
},
"modelCreatorName": {
"description": "name of the user who created the model",
"type": [
"string",
"null"
]
},
"modelName": {
"description": "model name",
"type": "string"
}
},
"required": [
"buildEnvironmentType",
"description",
"location"
],
"type": "object"
},
"modelExecutionType": {
"description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not",
"enum": [
"dedicated",
"custom_inference_model",
"external"
],
"type": "string"
},
"modelId": {
"description": "ID of the model",
"type": "string"
},
"modelKind": {
"description": "Model attribute information",
"properties": {
"isAnomalyDetectionModel": {
"description": "true if this is an anomaly detection model",
"type": "boolean"
},
"isCombinedModel": {
"description": "true if model is a combined model",
"type": "boolean",
"x-versionadded": "v2.27"
},
"isFeatureDiscovery": {
"description": "true if this model uses the Feature Discovery feature",
"type": "boolean"
},
"isMultiseries": {
"description": "true if model is multiseries",
"type": "boolean"
},
"isTimeSeries": {
"description": "true if model is time series",
"type": "boolean"
},
"isUnsupervisedLearning": {
"description": "true if model used unsupervised learning",
"type": "boolean"
}
},
"required": [
"isAnomalyDetectionModel",
"isCombinedModel",
"isFeatureDiscovery",
"isMultiseries",
"isTimeSeries",
"isUnsupervisedLearning"
],
"type": "object"
},
"name": {
"description": "Model package name",
"type": "string"
},
"permissions": {
"description": "List of action permissions the user making the request has on the model package",
"items": {
"type": "string"
},
"type": "array",
"x-versionadded": "v2.20"
},
"sourceMeta": {
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
},
"target": {
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
},
"timeseries": {
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
},
"updatedBy": {
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
},
"userProvidedId": {
"description": "A user-provided unique ID associated with the given custom inference model.",
"type": "string"
}
},
"required": [
"activeDeploymentCount",
"capabilities",
"datasets",
"id",
"importMeta",
"isArchived",
"isDeprecated",
"modelDescription",
"modelExecutionType",
"modelId",
"modelKind",
"name",
"permissions",
"sourceMeta",
"target",
"timeseries",
"updatedBy"
],
"type": "object"
}
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| activeDeploymentCount |
integer |
true |
|
Number of deployments currently using this model package |
| buildStatus |
string,null |
false |
|
Model package build status |
| capabilities |
ModelPackageCapabilities |
true |
|
Capabilities of the current model package. |
| datasets |
ModelPackageDatasets |
true |
|
dataset information for the model package |
| id |
string |
true |
|
ID of the Model package |
| importMeta |
ModelPackageImportMeta |
true |
|
Information from when this Model Package was first saved |
| isArchived |
boolean |
true |
|
Whether the model package is permanently archived (cannot be used in deployment or replacement) |
| isDeprecated |
boolean |
true |
|
Whether the model package is deprecated. eg. python2 models are deprecated. |
| mlpkgFileContents |
MlpkgFileContents |
false |
|
Information about the content of .mlpkg artifact |
| modelDescription |
ModelPackageModelDescription |
true |
|
model description information for the model package |
| modelExecutionType |
string |
true |
|
Type of model package. dedicated (native DataRobot models) and custom_inference_model (user added inference models) both execute on DataRobot prediction servers, external do not |
| modelId |
string |
true |
|
ID of the model |
| modelKind |
ModelPackageModelKind |
true |
|
Model attribute information |
| name |
string |
true |
|
Model package name |
| permissions |
[string] |
true |
|
List of action permissions the user making the request has on the model package |
| sourceMeta |
ModelPackageSourceMeta |
true |
|
Meta information from where this model was generated |
| target |
ModelPackageTarget |
true |
|
target information for the model package |
| timeseries |
ModelPackageTimeseries |
true |
|
time series information for the model package |
| updatedBy |
UserMetadata |
true |
|
Information on the user who last modified the registered model |
| userProvidedId |
string |
false |
|
A user-provided unique ID associated with the given custom inference model. |
Enumerated Values
| Property |
Value |
| buildStatus |
[inProgress, complete, failed] |
| modelExecutionType |
[dedicated, custom_inference_model, external] |
{
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
}
If available, information about the model's scoring code
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| dataRobotPredictionVersion |
string,null |
true |
|
DataRobot prediction API version for the scoring code |
| location |
string,null |
true |
|
Location of the scoring code |
Enumerated Values
| Property |
Value |
| location |
[local_leaderboard, mlpkg] |
{
"description": "Meta information from where this model was generated",
"properties": {
"customModelDetails": {
"description": "Details of the custom model associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when the custom model was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created the custom model",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the custom model",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created the custom model",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated custom model",
"type": "string"
},
"versionLabel": {
"description": "Label of associated custom model version.",
"type": [
"string",
"null"
],
"x-versionadded": "v2.34"
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
},
"environmentUrl": {
"description": "If available, URL of the source model",
"format": "uri",
"type": [
"string",
"null"
]
},
"fips_140_2Enabled": {
"description": "true if the model was built with FIPS-140-2",
"type": "boolean"
},
"projectCreatedAt": {
"description": "If available, time when the project was created",
"type": [
"string",
"null"
]
},
"projectCreatorEmail": {
"description": "If available, email of the user who created the project",
"type": [
"string",
"null"
]
},
"projectCreatorId": {
"default": null,
"description": "If available, ID of the creator of the project",
"type": [
"string",
"null"
]
},
"projectCreatorName": {
"description": "If available, name of the user who created the project",
"type": [
"string",
"null"
]
},
"projectId": {
"description": "If available, the project id used for this model",
"type": [
"string",
"null"
]
},
"projectName": {
"description": "If available, the project name for this model",
"type": [
"string",
"null"
]
},
"scoringCode": {
"description": "If available, information about the model's scoring code",
"properties": {
"dataRobotPredictionVersion": {
"description": "DataRobot prediction API version for the scoring code",
"type": [
"string",
"null"
]
},
"location": {
"description": "Location of the scoring code",
"enum": [
"local_leaderboard",
"mlpkg"
],
"type": [
"string",
"null"
]
}
},
"required": [
"dataRobotPredictionVersion",
"location"
],
"type": "object"
},
"useCaseDetails": {
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
},
"required": [
"environmentUrl",
"projectId",
"projectName",
"scoringCode"
],
"type": "object"
}
Meta information from where this model was generated
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| customModelDetails |
CustomModelDetails |
false |
|
Details of the custom model associated to this registered model version |
| environmentUrl |
string,null(uri) |
true |
|
If available, URL of the source model |
| fips_140_2Enabled |
boolean |
false |
|
true if the model was built with FIPS-140-2 |
| projectCreatedAt |
string,null |
false |
|
If available, time when the project was created |
| projectCreatorEmail |
string,null |
false |
|
If available, email of the user who created the project |
| projectCreatorId |
string,null |
false |
|
If available, ID of the creator of the project |
| projectCreatorName |
string,null |
false |
|
If available, name of the user who created the project |
| projectId |
string,null |
true |
|
If available, the project id used for this model |
| projectName |
string,null |
true |
|
If available, the project name for this model |
| scoringCode |
ModelPackageScoringCodeMeta |
true |
|
If available, information about the model's scoring code |
| useCaseDetails |
UseCaseDetails |
false |
|
Details of the use-case associated to this registered model version |
ModelPackageTarget
{
"description": "target information for the model package",
"properties": {
"classCount": {
"description": "Number of classes for classification models.",
"minimum": 0,
"type": [
"integer",
"null"
]
},
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.",
"items": {
"type": "string"
},
"maxItems": 100,
"type": "array"
},
"name": {
"description": "name of the target column",
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"classCount",
"classNames",
"name",
"predictionProbabilitiesColumn",
"predictionThreshold",
"type"
],
"type": "object"
}
target information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| classCount |
integer,null |
true |
minimum: 0
|
Number of classes for classification models. |
| classNames |
[string] |
true |
maxItems: 100
|
Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass. |
| name |
string |
true |
|
name of the target column |
| predictionProbabilitiesColumn |
string,null |
true |
|
Field or column name containing prediction probabilities |
| predictionThreshold |
number,null |
true |
maximum: 1
minimum: 0
|
Prediction threshold used for binary classification models |
| type |
string |
true |
|
Target type of the model. |
Enumerated Values
| Property |
Value |
| type |
[Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] |
ModelPackageTargetCreate
{
"description": "The target information for the model package.",
"properties": {
"classNames": {
"description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.",
"items": {
"maxLength": 128,
"type": "string"
},
"maxItems": 1000,
"type": "array"
},
"name": {
"description": "name of the target column",
"maxLength": 128,
"type": "string"
},
"predictionProbabilitiesColumn": {
"description": "Field or column name containing prediction probabilities",
"maxLength": 128,
"type": [
"string",
"null"
]
},
"predictionThreshold": {
"description": "Prediction threshold used for binary classification models",
"maximum": 1,
"minimum": 0,
"type": [
"number",
"null"
]
},
"type": {
"description": "Target type of the model.",
"enum": [
"Binary",
"Regression",
"Multiclass",
"Multilabel",
"TextGeneration",
"GeoPoint",
"AgenticWorkflow",
"MCP"
],
"type": "string"
}
},
"required": [
"name",
"type"
],
"type": "object",
"x-versionadded": "v2.37"
}
The target information for the model package.
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| classNames |
[string] |
false |
maxItems: 1000
|
Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. |
| name |
string |
true |
maxLength: 128
|
name of the target column |
| predictionProbabilitiesColumn |
string,null |
false |
maxLength: 128
|
Field or column name containing prediction probabilities |
| predictionThreshold |
number,null |
false |
maximum: 1
minimum: 0
|
Prediction threshold used for binary classification models |
| type |
string |
true |
|
Target type of the model. |
Enumerated Values
| Property |
Value |
| type |
[Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] |
ModelPackageTextGeneration
{
"description": "Text generation information for the model package",
"properties": {
"prompt": {
"description": "Name of the prompt column",
"type": [
"string",
"null"
]
}
},
"required": [
"prompt"
],
"type": "object",
"x-versionadded": "v2.37"
}
Text generation information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| prompt |
string,null |
true |
|
Name of the prompt column |
ModelPackageTimeseries
{
"description": "time series information for the model package",
"properties": {
"datetimeColumnFormat": {
"description": "Date format for forecast date and forecast point column",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "Name of the forecast date column",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"effectiveFeatureDerivationWindowStart": {
"description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.25"
},
"featureDerivationWindowEnd": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "Name of the forecast distance column",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "List of integer forecast distances",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": [
"string",
"null"
]
},
"forecastPointColumnName": {
"description": "Name of the forecast point column",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
],
"x-versionadded": "v2.25"
},
"isTraditionalTimeSeries": {
"description": "true if the model is traditional time series.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "Name of the series column in case of multi-series date",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"effectiveFeatureDerivationWindowEnd",
"effectiveFeatureDerivationWindowStart",
"featureDerivationWindowEnd",
"featureDerivationWindowStart",
"forecastDistanceColumnName",
"forecastDistances",
"forecastDistancesTimeUnit",
"forecastPointColumnName",
"isCrossSeries",
"isNewSeriesSupport",
"isTraditionalTimeSeries",
"seriesColumnName"
],
"type": "object"
}
time series information for the model package
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| datetimeColumnFormat |
string,null |
true |
|
Date format for forecast date and forecast point column |
| datetimeColumnName |
string,null |
true |
|
Name of the forecast date column |
| effectiveFeatureDerivationWindowEnd |
integer,null |
true |
maximum: 0
|
Same concept as featureDerivationWindowEnd which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| effectiveFeatureDerivationWindowStart |
integer,null |
true |
maximum: 0
|
Same concept as featureDerivationWindowStart which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| featureDerivationWindowEnd |
integer,null |
true |
maximum: 0
|
Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end. |
| featureDerivationWindowStart |
integer,null |
true |
maximum: 0
|
Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin. |
| forecastDistanceColumnName |
string,null |
true |
|
Name of the forecast distance column |
| forecastDistances |
[integer] |
true |
|
List of integer forecast distances |
| forecastDistancesTimeUnit |
string,null |
true |
|
The time unit of forecast distances |
| forecastPointColumnName |
string,null |
true |
|
Name of the forecast point column |
| isCrossSeries |
boolean,null |
true |
|
true if the model is cross-series. |
| isNewSeriesSupport |
boolean,null |
true |
|
true if the model is optimized to support new series. |
| isTraditionalTimeSeries |
boolean,null |
true |
|
true if the model is traditional time series. |
| seriesColumnName |
string,null |
true |
|
Name of the series column in case of multi-series date |
Enumerated Values
| Property |
Value |
| forecastDistancesTimeUnit |
[MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] |
ModelPackageTimeseriesCreate
{
"description": "Time series information for the model package.",
"properties": {
"datetimeColumnFormat": {
"description": "The date format for the forecast date and forecast point column.",
"type": [
"string",
"null"
]
},
"datetimeColumnName": {
"description": "The name of the forecast date column.",
"type": [
"string",
"null"
]
},
"effectiveFeatureDerivationWindowEnd": {
"description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"effectiveFeatureDerivationWindowStart": {
"description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.",
"maximum": 0,
"type": [
"integer",
"null"
],
"x-versionadded": "v2.37"
},
"featureDerivationWindowEnd": {
"description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"featureDerivationWindowStart": {
"description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.",
"maximum": 0,
"type": [
"integer",
"null"
]
},
"forecastDistanceColumnName": {
"description": "The name of the forecast distance column.",
"type": [
"string",
"null"
]
},
"forecastDistances": {
"description": "A list of integer forecast distances.",
"items": {
"type": "integer"
},
"type": "array"
},
"forecastDistancesTimeUnit": {
"description": "The time unit of forecast distances.",
"enum": [
"MICROSECOND",
"MILLISECOND",
"SECOND",
"MINUTE",
"HOUR",
"DAY",
"WEEK",
"MONTH",
"QUARTER",
"YEAR"
],
"type": "string"
},
"forecastPointColumnName": {
"description": "The name of the forecast point column.",
"type": [
"string",
"null"
]
},
"isCrossSeries": {
"description": "true if the model is cross-series.",
"type": [
"boolean",
"null"
]
},
"isNewSeriesSupport": {
"default": false,
"description": "true if the model is optimized to support new series.",
"type": [
"boolean",
"null"
]
},
"isTraditionalTimeSeries": {
"default": false,
"description": "Determines if the model is a traditional time series model.",
"type": [
"boolean",
"null"
]
},
"seriesColumnName": {
"description": "The name of the series column in the case of a multi-series date.",
"type": [
"string",
"null"
]
}
},
"required": [
"datetimeColumnFormat",
"datetimeColumnName",
"forecastDistanceColumnName",
"forecastDistancesTimeUnit",
"forecastPointColumnName"
],
"type": "object",
"x-versionadded": "v2.37"
}
Time series information for the model package.
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| datetimeColumnFormat |
string,null |
true |
|
The date format for the forecast date and forecast point column. |
| datetimeColumnName |
string,null |
true |
|
The name of the forecast date column. |
| effectiveFeatureDerivationWindowEnd |
integer,null |
false |
maximum: 0
|
A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the "real" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| effectiveFeatureDerivationWindowStart |
integer,null |
false |
maximum: 0
|
A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the "real" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. |
| featureDerivationWindowEnd |
integer,null |
false |
maximum: 0
|
A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago. |
| featureDerivationWindowStart |
integer,null |
false |
maximum: 0
|
A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago. |
| forecastDistanceColumnName |
string,null |
true |
|
The name of the forecast distance column. |
| forecastDistances |
[integer] |
false |
|
A list of integer forecast distances. |
| forecastDistancesTimeUnit |
string |
true |
|
The time unit of forecast distances. |
| forecastPointColumnName |
string,null |
true |
|
The name of the forecast point column. |
| isCrossSeries |
boolean,null |
false |
|
true if the model is cross-series. |
| isNewSeriesSupport |
boolean,null |
false |
|
true if the model is optimized to support new series. |
| isTraditionalTimeSeries |
boolean,null |
false |
|
Determines if the model is a traditional time series model. |
| seriesColumnName |
string,null |
false |
|
The name of the series column in the case of a multi-series date. |
Enumerated Values
| Property |
Value |
| forecastDistancesTimeUnit |
[MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] |
{
"properties": {
"data": {
"description": "An archive (tar.gz) of the logs produced and persisted by a model.",
"format": "binary",
"type": "string"
}
},
"required": [
"data"
],
"type": "object"
}
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| data |
string(binary) |
true |
|
An archive (tar.gz) of the logs produced and persisted by a model. |
UseCaseDetails
{
"description": "Details of the use-case associated to this registered model version",
"properties": {
"createdAt": {
"description": "Time when use-case was created",
"type": "string"
},
"creatorEmail": {
"description": "Email of the user who created use-case",
"type": [
"string",
"null"
]
},
"creatorId": {
"description": "ID of the creator of the use-case",
"type": "string"
},
"creatorName": {
"description": "Name of the user who created use-case",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the associated use-case",
"type": "string"
},
"name": {
"description": "Name of the use case at the moment of creation",
"type": [
"string",
"null"
]
}
},
"required": [
"createdAt",
"creatorId",
"id"
],
"type": "object"
}
Details of the use-case associated to this registered model version
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| createdAt |
string |
true |
|
Time when use-case was created |
| creatorEmail |
string,null |
false |
|
Email of the user who created use-case |
| creatorId |
string |
true |
|
ID of the creator of the use-case |
| creatorName |
string,null |
false |
|
Name of the user who created use-case |
| id |
string |
true |
|
ID of the associated use-case |
| name |
string,null |
false |
|
Name of the use case at the moment of creation |
{
"description": "Information on the user who last modified the registered model",
"properties": {
"email": {
"description": "Email of the user",
"type": [
"string",
"null"
]
},
"id": {
"description": "ID of the user",
"type": "string"
},
"name": {
"description": "Full name of the user",
"type": [
"string",
"null"
]
}
},
"required": [
"email",
"id",
"name"
],
"type": "object"
}
Information on the user who last modified the registered model
Properties
| Name |
Type |
Required |
Restrictions |
Description |
| email |
string,null |
true |
|
Email of the user |
| id |
string |
true |
|
ID of the user |
| name |
string,null |
true |
|
Full name of the user |