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:null.- Items: Refer to #/definitions/mlFeature.
mlHyperParameters(array): Hyper Parameters used to train the ML Model. Default:null.- 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:null.- Items: Refer to ../../type/tagLabel.json.
usageSummary: Latest usage information for this ML Model. Refer to ../../type/usageDetails.json. Default:null.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.deleted(boolean): Whentrueindicates 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.domain: Domain the MLModel belongs to. When not set, the MLModel inherits the domain from the ML Model Service it belongs to. Refer to ../../type/entityReference.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:null.- 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:null.- 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.