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.



13688
13689
13690
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13688

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) 


13543
13544
13545
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13543

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:



13551
13552
13553
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13551

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) 


13556
13557
13558
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13556

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) 


13563
13564
13565
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13563

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) 


13571
13572
13573
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13571

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:



13578
13579
13580
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13578

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) 


13584
13585
13586
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13584

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:



13594
13595
13596
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13594

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>) 


13603
13604
13605
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13603

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:



13608
13609
13610
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13608

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) 


13615
13616
13617
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13615

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:



13620
13621
13622
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13620

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:



13626
13627
13628
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13626

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:



13631
13632
13633
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13631

def model_deployment_monitoring_schedule_config
  @model_deployment_monitoring_schedule_config
end

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

Alert config for model monitoring. Corresponds to the JSON property modelMonitoringAlertConfig

Returns:



13636
13637
13638
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13636

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) 


13641
13642
13643
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13641

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) 


13647
13648
13649
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13647

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) 


13654
13655
13656
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13654

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) 


13662
13663
13664
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13662

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) 


13667
13668
13669
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13667

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) 


13675
13676
13677
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13675

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:



13680
13681
13682
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13680

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) 


13686
13687
13688
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13686

def update_time
  @update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



13693
13694
13695
13696
13697
13698
13699
13700
13701
13702
13703
13704
13705
13706
13707
13708
13709
13710
13711
13712
13713
13714
13715
13716
13717
 
# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 13693

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