GetModelDeploymentMonitoringJobResult class

Result data returned by getModelDeploymentMonitoringJob.

Constructors

GetModelDeploymentMonitoringJobResult({required String analysisInstanceSchemaUri, required List<GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse> bigqueryTables, required String createTime, required String displayName, required bool enableMonitoringPipelineLogs, required GoogleCloudAiplatformV1EncryptionSpecResponse encryptionSpec, required String endpoint, required GoogleRpcStatusResponse error, required Map<String, String> labels, required GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse latestMonitoringPipelineMetadata, required String logTtl, required GoogleCloudAiplatformV1SamplingStrategyResponse loggingSamplingStrategy, required List<GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse> modelDeploymentMonitoringObjectiveConfigs, required GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse modelDeploymentMonitoringScheduleConfig, required GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse modelMonitoringAlertConfig, required String name, required String nextScheduleTime, required String predictInstanceSchemaUri, required dynamic samplePredictInstance, required String scheduleState, required String state, required GoogleCloudAiplatformV1GcsDestinationResponse statsAnomaliesBaseDirectory, required String updateTime})
Creates a new GetModelDeploymentMonitoringJobResult. analysisInstanceSchemaUri YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string. bigqueryTables The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response createTime Timestamp when this ModelDeploymentMonitoringJob was created. displayName The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob. enableMonitoringPipelineLogs If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing. encryptionSpec Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key. endpoint Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint} error Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED. labels The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. latestMonitoringPipelineMetadata Latest triggered monitoring pipeline metadata. logTtl The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day. loggingSamplingStrategy Sample Strategy for logging. modelDeploymentMonitoringObjectiveConfigs The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately. modelDeploymentMonitoringScheduleConfig Schedule config for running the monitoring job. modelMonitoringAlertConfig Alert config for model monitoring. name Resource name of a ModelDeploymentMonitoringJob. nextScheduleTime Timestamp when this monitoring pipeline will be scheduled to run for the next round. predictInstanceSchemaUri YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests. samplePredictInstance Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests. scheduleState Schedule state when the monitoring job is in Running state. state The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'. statsAnomaliesBaseDirectory Stats anomalies base folder path. updateTime Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
const
GetModelDeploymentMonitoringJobResult.fromMap(Map<String, dynamic> map)
factory

Properties

analysisInstanceSchemaUri → String
YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
final
bigqueryTables → List<GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTableResponse>
The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response
final
createTime → String
Timestamp when this ModelDeploymentMonitoringJob was created.
final
displayName → String
The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.
final
enableMonitoringPipelineLogs → bool
If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing.
final
encryptionSpec → GoogleCloudAiplatformV1EncryptionSpecResponse
Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.
final
endpoint → String
Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
final
error → GoogleRpcStatusResponse
Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
final
hashCode → int
The hash code for this object.
no setterinherited
labels → Map<String, String>
The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
final
latestMonitoringPipelineMetadata → GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadataResponse
Latest triggered monitoring pipeline metadata.
final
loggingSamplingStrategy → GoogleCloudAiplatformV1SamplingStrategyResponse
Sample Strategy for logging.
final
logTtl → String
The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.
final
modelDeploymentMonitoringObjectiveConfigs → List<GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfigResponse>
The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.
final
modelDeploymentMonitoringScheduleConfig → GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfigResponse
Schedule config for running the monitoring job.
final
modelMonitoringAlertConfig → GoogleCloudAiplatformV1ModelMonitoringAlertConfigResponse
Alert config for model monitoring.
final
name → String
Resource name of a ModelDeploymentMonitoringJob.
final
nextScheduleTime → String
Timestamp when this monitoring pipeline will be scheduled to run for the next round.
final
predictInstanceSchemaUri → String
YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
samplePredictInstance → dynamic
Sample Predict instance, same format as PredictRequest.instances, this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri. If not set, we will generate predict schema from collected predict requests.
final
scheduleState → String
Schedule state when the monitoring job is in Running state.
final
state → String
The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.
final
statsAnomaliesBaseDirectory → GoogleCloudAiplatformV1GcsDestinationResponse
Stats anomalies base folder path.
final
updateTime → String
Timestamp when this ModelDeploymentMonitoringJob was updated most recently.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited