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): Whentrue
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.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.
Documentation file automatically generated at 2025-01-15 09:05:25.266839+00:00.