Represents information about the machine learning models that generate predictions for your use case. This object is available in API version 55.0 and later.
The AIUsecaseFieldMapping object is available when the admin settings for AI Accelerator and for the product related to the use case are enabled. The Salesforce org must have the CRM Plus license and the product’s CRM license.
Description: The ID of the feature extractor that’s used by the use case model.
This field is a relationship field.
Relationship Name: AiFeatureExtractor
Relationship Type: Lookup
Refers To: AIFeatureExtractor
AiUsecaseDefinitionId
Type: reference
Properties: Create, Filter, Group, Sort, Update
Description: Required. The ID of the use case definition that’s related to the use case model.
This field is a relationship field.
Relationship Name: AiUsecaseDefinition
Relationship Type: Lookup
Refers To: AIUsecaseDefinition
ArePredctGenInRealTime
Type: boolean
Properties: Defaulted on create, Filter, Group, Sort, Update
Description: Required. Indicates whether the prediction results are generated in real time by using AI Accelerator or whether they are generated by Data Cloud and stored in a Data Model Object. If this attribute is set to false, AI Accelerator reads and returns already generated prediction results from Data Cloud.
Default value: True
DeveloperName
Type: string
Properties: Create, Filter, Group, Sort, Update
Description: The developer name of the use case model.
Description: Required. The unique identifier of the prediction definition that’s related to the use case model. This identifier can be an external ID. If you use Einstein Discovery to create models, this field stores the developer name of the record.
Description: Required. The platform on which the machine learning model is created and deployed. Available in API version 57.0 and later.
Default value: Einstein_Discovery
Possible values:
Default
Einstein_Discovery
Usage
Use this object to configure the machine learning model for your use case. A use case can have multiple models for better performance or for changing the model’s objective.