RecommendationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)Additional information of a recommendation engine.
Attributes | |
|---|---|
| Name | Description |
serving_state |
google.cloud.discoveryengine_v1alpha.types.Engine.RecommendationMetadata.ServingState
Output only. The serving state of the engine: ACTIVE,
NOT_ACTIVE.
|
data_state |
google.cloud.discoveryengine_v1alpha.types.Engine.RecommendationMetadata.DataState
Output only. The state of data requirements for this engine: DATA_OK and DATA_ERROR.
Engine cannot be trained if the data is in DATA_ERROR
state. Engine can have DATA_ERROR state even if serving
state is ACTIVE: engines were trained successfully
before, but cannot be refreshed because the underlying
engine no longer has sufficient data for training.
|
last_tune_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. The timestamp when the latest successful tune finished. Only applicable on Media Recommendation engines. |
tuning_operation |
str
Output only. The latest tune operation id associated with the engine. Only applicable on Media Recommendation engines. If present, this operation id can be used to determine if there is an ongoing tune for this engine. To check the operation status, send the GetOperation request with this operation id in the engine resource format. If no tuning has happened for this engine, the string is empty. |
Classes
DataState
DataState(value)Describes whether this engine have sufficient training data to be continuously trained.
Values: DATA_STATE_UNSPECIFIED (0): Unspecified default value, should never be explicitly set. DATA_OK (1): The engine has sufficient training data. DATA_ERROR (2): The engine does not have sufficient training data. Error messages can be queried via Stackdriver.
ServingState
ServingState(value)The serving state of the recommendation engine.
Values: SERVING_STATE_UNSPECIFIED (0): Unspecified serving state. INACTIVE (1): The engine is not serving. ACTIVE (2): The engine is serving and can be queried. TUNED (3): The engine is trained on tuned hyperparameters and can be queried.