|
26 | 26 | from google.cloud.aiplatform_v1beta1.types import (
|
27 | 27 | manual_batch_tuning_parameters as gca_manual_batch_tuning_parameters,
|
28 | 28 | )
|
| 29 | +from google.cloud.aiplatform_v1beta1.types import model_deployment_monitoring_job |
29 | 30 | from google.cloud.aiplatform_v1beta1.types import model_monitoring
|
30 | 31 | from google.cloud.aiplatform_v1beta1.types import (
|
31 | 32 | unmanaged_container_model as gca_unmanaged_container_model,
|
@@ -220,6 +221,12 @@ class BatchPredictionJob(proto.Message):
|
220 | 221 | analysis model behaviors, based on the input and
|
221 | 222 | output to the batch prediction job, as well as
|
222 | 223 | the provided training dataset.
|
| 224 | + model_monitoring_stats_anomalies (Sequence[google.cloud.aiplatform_v1beta1.types.ModelMonitoringStatsAnomalies]): |
| 225 | + Get batch prediction job monitoring |
| 226 | + statistics. |
| 227 | + model_monitoring_status (google.rpc.status_pb2.Status): |
| 228 | + Output only. The running status of the model |
| 229 | + monitoring pipeline. |
223 | 230 | """
|
224 | 231 |
|
225 | 232 | class InputConfig(proto.Message):
|
@@ -539,6 +546,16 @@ class OutputInfo(proto.Message):
|
539 | 546 | number=26,
|
540 | 547 | message=model_monitoring.ModelMonitoringConfig,
|
541 | 548 | )
|
| 549 | + model_monitoring_stats_anomalies = proto.RepeatedField( |
| 550 | + proto.MESSAGE, |
| 551 | + number=31, |
| 552 | + message=model_deployment_monitoring_job.ModelMonitoringStatsAnomalies, |
| 553 | + ) |
| 554 | + model_monitoring_status = proto.Field( |
| 555 | + proto.MESSAGE, |
| 556 | + number=32, |
| 557 | + message=status_pb2.Status, |
| 558 | + ) |
542 | 559 |
|
543 | 560 |
|
544 | 561 | __all__ = tuple(sorted(__protobuf__.manifest))
|
0 commit comments