Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJob

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb

Overview

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJob

Returns a new instance of GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJob.



14183
14184
14185
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14183

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#analysis_instance_schema_uri ⇒ 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. Corresponds to the JSON property analysisInstanceSchemaUri

Returns:

  • (String) 


14038
14039
14040
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14038

def analysis_instance_schema_uri
  @analysis_instance_schema_uri
end

#bigquery_tables ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringBigQueryTable>

Output only. 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 Corresponds to the JSON property bigqueryTables

Returns:



14046
14047
14048
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14046

def bigquery_tables
  @bigquery_tables
end

#create_time ⇒ String

Output only. Timestamp when this ModelDeploymentMonitoringJob was created. Corresponds to the JSON property createTime

Returns:

  • (String) 


14051
14052
14053
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14051

def create_time
  @create_time
end

#display_name ⇒ String

Required. 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. Corresponds to the JSON property displayName

Returns:

  • (String) 


14058
14059
14060
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14058

def display_name
  @display_name
end

#enable_monitoring_pipeline_logs ⇒ Boolean Also known as: enable_monitoring_pipeline_logs?

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. Corresponds to the JSON property enableMonitoringPipelineLogs

Returns:

  • (Boolean) 


14066
14067
14068
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14066

def enable_monitoring_pipeline_logs
  @enable_monitoring_pipeline_logs
end

#encryption_spec ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top- level resource. Corresponds to the JSON property encryptionSpec

Returns:



14073
14074
14075
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14073

def encryption_spec
  @encryption_spec
end

#endpoint ⇒ String

Required. Endpoint resource name. Format: projects/project/locations/ location/endpoints/endpoint` Corresponds to the JSON propertyendpoint`

Returns:

  • (String) 


14079
14080
14081
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14079

def endpoint
  @endpoint
end

#error ⇒ Google::Apis::AiplatformV1beta1::GoogleRpcStatus

The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC. Each Status message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the API Design Guide. Corresponds to the JSON property error

Returns:



14089
14090
14091
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14089

def error
  @error
end

#labels ⇒ Hash<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. Corresponds to the JSON property labels

Returns:

  • (Hash<String,String>) 


14098
14099
14100
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14098

def labels
  @labels
end

#latest_monitoring_pipeline_metadata ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines. Corresponds to the JSON property latestMonitoringPipelineMetadata

Returns:



14103
14104
14105
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14103

def latest_monitoring_pipeline_metadata
  @latest_monitoring_pipeline_metadata
end

#log_ttl ⇒ 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. Corresponds to the JSON property logTtl

Returns:

  • (String) 


14110
14111
14112
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14110

def log_ttl
  @log_ttl
end

#logging_sampling_strategy ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset. Corresponds to the JSON property loggingSamplingStrategy

Returns:



14115
14116
14117
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14115

def logging_sampling_strategy
  @logging_sampling_strategy
end

#model_deployment_monitoring_objective_configs ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringObjectiveConfig>

Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately. Corresponds to the JSON property modelDeploymentMonitoringObjectiveConfigs

Returns:



14121
14122
14123
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14121

def model_deployment_monitoring_objective_configs
  @model_deployment_monitoring_objective_configs
end

#model_deployment_monitoring_schedule_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job. Corresponds to the JSON property modelDeploymentMonitoringScheduleConfig

Returns:



14126
14127
14128
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14126

def model_deployment_monitoring_schedule_config
  @model_deployment_monitoring_schedule_config
end

#model_monitoring_alert_config ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig

The alert config for model monitoring. Corresponds to the JSON property modelMonitoringAlertConfig

Returns:



14131
14132
14133
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14131

def model_monitoring_alert_config
  @model_monitoring_alert_config
end

#name ⇒ String

Output only. Resource name of a ModelDeploymentMonitoringJob. Corresponds to the JSON property name

Returns:

  • (String) 


14136
14137
14138
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14136

def name
  @name
end

#next_schedule_time ⇒ String

Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round. Corresponds to the JSON property nextScheduleTime

Returns:

  • (String) 


14142
14143
14144
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14142

def next_schedule_time
  @next_schedule_time
end

#predict_instance_schema_uri ⇒ 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. Corresponds to the JSON property predictInstanceSchemaUri

Returns:

  • (String) 


14149
14150
14151
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14149

def predict_instance_schema_uri
  @predict_instance_schema_uri
end

#sample_predict_instance ⇒ Object

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. Corresponds to the JSON property samplePredictInstance

Returns:

  • (Object) 


14157
14158
14159
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14157

def sample_predict_instance
  @sample_predict_instance
end

#schedule_state ⇒ String

Output only. Schedule state when the monitoring job is in Running state. Corresponds to the JSON property scheduleState

Returns:

  • (String) 


14162
14163
14164
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14162

def schedule_state
  @schedule_state
end

#state ⇒ String

Output only. 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'. Corresponds to the JSON property state

Returns:

  • (String) 


14170
14171
14172
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14170

def state
  @state
end

#stats_anomalies_base_directory ⇒ Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1GcsDestination

The Google Cloud Storage location where the output is to be written to. Corresponds to the JSON property statsAnomaliesBaseDirectory

Returns:



14175
14176
14177
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14175

def stats_anomalies_base_directory
  @stats_anomalies_base_directory
end

#update_time ⇒ String

Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently. Corresponds to the JSON property updateTime

Returns:

  • (String) 


14181
14182
14183
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14181

def update_time
  @update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



14188
14189
14190
14191
14192
14193
14194
14195
14196
14197
14198
14199
14200
14201
14202
14203
14204
14205
14206
14207
14208
14209
14210
14211
14212
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 14188

def update!(**args)
  @analysis_instance_schema_uri = args[:analysis_instance_schema_uri] if args.key?(:analysis_instance_schema_uri)
  @bigquery_tables = args[:bigquery_tables] if args.key?(:bigquery_tables)
  @create_time = args[:create_time] if args.key?(:create_time)
  @display_name = args[:display_name] if args.key?(:display_name)
  @enable_monitoring_pipeline_logs = args[:enable_monitoring_pipeline_logs] if args.key?(:enable_monitoring_pipeline_logs)
  @encryption_spec = args[:encryption_spec] if args.key?(:encryption_spec)
  @endpoint = args[:endpoint] if args.key?(:endpoint)
  @error = args[:error] if args.key?(:error)
  @labels = args[:labels] if args.key?(:labels)
  @latest_monitoring_pipeline_metadata = args[:latest_monitoring_pipeline_metadata] if args.key?(:latest_monitoring_pipeline_metadata)
  @log_ttl = args[:log_ttl] if args.key?(:log_ttl)
  @logging_sampling_strategy = args[:logging_sampling_strategy] if args.key?(:logging_sampling_strategy)
  @model_deployment_monitoring_objective_configs = args[:model_deployment_monitoring_objective_configs] if args.key?(:model_deployment_monitoring_objective_configs)
  @model_deployment_monitoring_schedule_config = args[:model_deployment_monitoring_schedule_config] if args.key?(:model_deployment_monitoring_schedule_config)
  @model_monitoring_alert_config = args[:model_monitoring_alert_config] if args.key?(:model_monitoring_alert_config)
  @name = args[:name] if args.key?(:name)
  @next_schedule_time = args[:next_schedule_time] if args.key?(:next_schedule_time)
  @predict_instance_schema_uri = args[:predict_instance_schema_uri] if args.key?(:predict_instance_schema_uri)
  @sample_predict_instance = args[:sample_predict_instance] if args.key?(:sample_predict_instance)
  @schedule_state = args[:schedule_state] if args.key?(:schedule_state)
  @state = args[:state] if args.key?(:state)
  @stats_anomalies_base_directory = args[:stats_anomalies_base_directory] if args.key?(:stats_anomalies_base_directory)
  @update_time = args[:update_time] if args.key?(:update_time)
end