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Home/Main Concepts/Metadata Standard/Schemas/Entity/Data/Mlmodel
OpenMetadata Documentation

MlModel

This schema defines the Model entity. Machine Learning Models are algorithms trained on data to find patterns or make predictions.

Properties

  • id: Unique identifier of an ML Model instance. Refer to ../../type/basic.json#/definitions/uuid.
  • name: Name that identifies this ML Model. Refer to ../../type/basic.json#/definitions/entityName.
  • fullyQualifiedName: A unique name that identifies an ML Model. Refer to ../../type/basic.json#/definitions/fullyQualifiedEntityName.
  • displayName (string): Display Name that identifies this ML Model.
  • description: Description of the ML Model, what it is, and how to use it. Refer to ../../type/basic.json#/definitions/markdown.
  • algorithm (string): Algorithm used to train the ML Model.
  • mlFeatures (array): Features used to train the ML Model. Default: None.
    • Items: Refer to #/definitions/mlFeature.
  • mlHyperParameters (array): Hyper Parameters used to train the ML Model. Default: None.
    • Items: Refer to #/definitions/mlHyperParameter.
  • target: For supervised ML Models, the value to estimate. Refer to ../../type/basic.json#/definitions/entityName.
  • dashboard: Performance Dashboard URL to track metric evolution. Refer to ../../type/entityReference.json.
  • mlStore: Location containing the ML Model. It can be a storage layer and/or a container repository. Refer to #/definitions/mlStore.
  • server: Endpoint that makes the ML Model available, e.g,. a REST API serving the data or computing predictions. Refer to ../../type/basic.json#/definitions/href.
  • href: Link to the resource corresponding to this entity. Refer to ../../type/basic.json#/definitions/href.
  • owners: Owners of this ML Model. Refer to ../../type/entityReferenceList.json.
  • followers: Followers of this ML Model. Refer to ../../type/entityReferenceList.json.
  • tags (array): Tags for this ML Model. Default: None.
    • Items: Refer to ../../type/tagLabel.json.
  • usageSummary: Latest usage information for this ML Model. Refer to ../../type/usageDetails.json. Default: None.
  • version: Metadata version of the entity. Refer to ../../type/entityHistory.json#/definitions/entityVersion.
  • updatedAt: Last update time corresponding to the new version of the entity in Unix epoch time milliseconds. Refer to ../../type/basic.json#/definitions/timestamp.
  • updatedBy (string): User who made the update.
  • service: Link to service where this pipeline is hosted in. Refer to ../../type/entityReference.json.
  • serviceType: Service type where this pipeline is hosted in. Refer to ../services/mlmodelService.json#/definitions/mlModelServiceType.
  • changeDescription: Change that lead to this version of the entity. Refer to ../../type/entityHistory.json#/definitions/changeDescription.
  • incrementalChangeDescription: Change that lead to this version of the entity. Refer to ../../type/entityHistory.json#/definitions/changeDescription.
  • deleted (boolean): When true indicates the entity has been soft deleted. Default: False.
  • extension: Entity extension data with custom attributes added to the entity. Refer to ../../type/basic.json#/definitions/entityExtension.
  • sourceUrl: Source URL of mlModel. Refer to ../../type/basic.json#/definitions/sourceUrl.
  • domains: Domains the MLModel belongs to. When not set, the MLModel inherits the domain from the ML Model Service it belongs to. Refer to ../../type/entityReferenceList.json.
  • dataProducts: List of data products this entity is part of. Refer to ../../type/entityReferenceList.json.
  • votes: Votes on the entity. Refer to ../../type/votes.json.
  • lifeCycle: Life Cycle properties of the entity. Refer to ../../type/lifeCycle.json.
  • certification: Refer to ../../type/assetCertification.json.
  • sourceHash (string): Source hash of the entity.

Definitions

  • featureType (string): This enum defines the type of data stored in a ML Feature. Must be one of: ['numerical', 'categorical'].
  • featureSourceDataType (string): This enum defines the type of data of a ML Feature source. Must be one of: ['integer', 'number', 'string', 'array', 'date', 'timestamp', 'object', 'boolean'].
  • featureSource (object): This schema defines the sources of a ML Feature. Cannot contain additional properties.
    • name: Refer to ../../type/basic.json#/definitions/entityName.
    • dataType: Data type of the source (int, date etc.). Refer to #/definitions/featureSourceDataType.
    • description: Description of the feature source. Refer to ../../type/basic.json#/definitions/markdown.
    • fullyQualifiedName: Refer to ../../type/basic.json#/definitions/fullyQualifiedEntityName.
    • dataSource: Description of the Data Source (e.g., a Table). Refer to ../../type/entityReference.json.
    • tags (array): Tags associated with the feature source. Default: [].
      • Items: Refer to ../../type/tagLabel.json.
  • mlFeature (object): This schema defines the type for an ML Feature used in an ML Model. Cannot contain additional properties.
    • name: Refer to ../../type/basic.json#/definitions/entityName.
    • dataType: Data type of the column (numerical vs. categorical). Refer to #/definitions/featureType.
    • description: Description of the ML Feature. Refer to ../../type/basic.json#/definitions/markdown.
    • fullyQualifiedName: Refer to ../../type/basic.json#/definitions/fullyQualifiedEntityName.
    • featureSources (array): Columns used to create the ML Feature. Default: None.
      • Items: Refer to #/definitions/featureSource.
    • featureAlgorithm (string): Description of the algorithm used to compute the feature, e.g., PCA, bucketing...
    • tags (array): Tags associated with the feature. Default: None.
      • Items: Refer to ../../type/tagLabel.json.
  • mlHyperParameter (object): This schema defines the type for an ML HyperParameter used in an ML Model. Cannot contain additional properties.
    • name (string): Hyper parameter name.
    • value (string): Hyper parameter value.
    • description: Description of the Hyper Parameter. Refer to ../../type/basic.json#/definitions/markdown.
  • mlStore (object): Location containing the ML Model. It can be a storage layer and/or a container repository. Cannot contain additional properties.
    • storage (string): Storage Layer containing the ML Model data.
    • imageRepository (string): Container Repository with the ML Model image.

Documentation file automatically generated at 2025-08-12 05:39:47.683420+00:00.