Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelMonitoringSchema

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

The Model Monitoring Schema definition.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1ModelMonitoringSchema

Returns a new instance of GoogleCloudAiplatformV1beta1ModelMonitoringSchema.



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 20233

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

Instance Attribute Details

#feature_fields ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema>

Feature names of the model. Vertex AI will try to match the features from your dataset as follows: * For 'csv' files, the header names are required, and we will extract the corresponding feature values when the header names align with the feature names. * For 'jsonl' files, we will extract the corresponding feature values if the key names match the feature names. Note: Nested features are not supported, so please ensure your features are flattened. Ensure the feature values are scalar or an array of scalars. * For 'bigquery' dataset, we will extract the corresponding feature values if the column names match the feature names. Note: The column type can be a scalar or an array of scalars. STRUCT or JSON types are not supported. You may use SQL queries to select or aggregate the relevant features from your original table. However, ensure that the 'schema' of the query results meets our requirements. * For the Vertex AI Endpoint Request Response Logging table or Vertex AI Batch Prediction Job results. If the instance_type is an array, ensure that the sequence in feature_fields matches the order of features in the prediction instance. We will match the feature with the array in the order specified in [ feature_fields]. Corresponds to the JSON property featureFields

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 20216

def feature_fields
  @feature_fields
end

#ground_truth_fields ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema>

Target /ground truth names of the model. Corresponds to the JSON property groundTruthFields

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 20221

def ground_truth_fields
  @ground_truth_fields
end

#prediction_fields ⇒ Array<Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1ModelMonitoringSchemaFieldSchema>

Prediction output names of the model. The requirements are the same as the feature_fields. For AutoML Tables, the prediction output name presented in schema will be: predicted_target_column`, thetarget_column` is the one you specified when you train the model. For Prediction output drift analysis: * AutoML Classification, the distribution of the argmax label will be analyzed.

  • AutoML Regression, the distribution of the value will be analyzed. Corresponds to the JSON property predictionFields

Returns:



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 20231

def prediction_fields
  @prediction_fields
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 20238

def update!(**args)
  @feature_fields = args[:feature_fields] if args.key?(:feature_fields)
  @ground_truth_fields = args[:ground_truth_fields] if args.key?(:ground_truth_fields)
  @prediction_fields = args[:prediction_fields] if args.key?(:prediction_fields)
end