Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/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) ⇒ GoogleCloudAiplatformV1ModelDeploymentMonitoringJob

Returns a new instance of GoogleCloudAiplatformV1ModelDeploymentMonitoringJob.



17784
17785
17786
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17784

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) 


17627
17628
17629
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17627

def analysis_instance_schema_uri
  @analysis_instance_schema_uri
end

#bigquery_tables ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDeploymentMonitoringBigQueryTable>

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:



17635
17636
17637
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17635

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) 


17640
17641
17642
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17640

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) 


17647
17648
17649
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17647

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) 


17655
17656
17657
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17655

def enable_monitoring_pipeline_logs
  @enable_monitoring_pipeline_logs
end

#encryption_spec ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1EncryptionSpec

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

Returns:



17662
17663
17664
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17662

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) 


17668
17669
17670
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17668

def endpoint
  @endpoint
end

#error ⇒ Google::Apis::AiplatformV1::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:



17678
17679
17680
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17678

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>) 


17687
17688
17689
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17687

def labels
  @labels
end

#latest_monitoring_pipeline_metadata ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDeploymentMonitoringJobLatestMonitoringPipelineMetadata

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

Returns:



17692
17693
17694
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17692

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) 


17699
17700
17701
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17699

def log_ttl
  @log_ttl
end

#logging_sampling_strategy ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1SamplingStrategy

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

Returns:



17704
17705
17706
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17704

def logging_sampling_strategy
  @logging_sampling_strategy
end

#model_deployment_monitoring_objective_configs ⇒ Array<Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDeploymentMonitoringObjectiveConfig>

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:



17710
17711
17712
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17710

def model_deployment_monitoring_objective_configs
  @model_deployment_monitoring_objective_configs
end

#model_deployment_monitoring_schedule_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelDeploymentMonitoringScheduleConfig

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

Returns:



17715
17716
17717
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17715

def model_deployment_monitoring_schedule_config
  @model_deployment_monitoring_schedule_config
end

#model_monitoring_alert_config ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1ModelMonitoringAlertConfig

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

Returns:



17720
17721
17722
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17720

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) 


17725
17726
17727
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17725

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) 


17731
17732
17733
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17731

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) 


17738
17739
17740
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17738

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) 


17746
17747
17748
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17746

def sample_predict_instance
  @sample_predict_instance
end

#satisfies_pzi ⇒ Boolean Also known as: satisfies_pzi?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzi

Returns:

  • (Boolean) 


17751
17752
17753
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17751

def satisfies_pzi
  @satisfies_pzi
end

#satisfies_pzs ⇒ Boolean Also known as: satisfies_pzs?

Output only. Reserved for future use. Corresponds to the JSON property satisfiesPzs

Returns:

  • (Boolean) 


17757
17758
17759
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17757

def satisfies_pzs
  @satisfies_pzs
end

#schedule_state ⇒ String

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

Returns:

  • (String) 


17763
17764
17765
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17763

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) 


17771
17772
17773
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17771

def state
  @state
end

#stats_anomalies_base_directory ⇒ Google::Apis::AiplatformV1::GoogleCloudAiplatformV1GcsDestination

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

Returns:



17776
17777
17778
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17776

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) 


17782
17783
17784
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17782

def update_time
  @update_time
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



17789
17790
17791
17792
17793
17794
17795
17796
17797
17798
17799
17800
17801
17802
17803
17804
17805
17806
17807
17808
17809
17810
17811
17812
17813
17814
17815
 
# File 'lib/google/apis/aiplatform_v1/classes.rb', line 17789

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)
  @satisfies_pzi = args[:satisfies_pzi] if args.key?(:satisfies_pzi)
  @satisfies_pzs = args[:satisfies_pzs] if args.key?(:satisfies_pzs)
  @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