Package com.google.cloud.aiplatform.v1beta1

Interface ModelMonitoringSchemaOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
ModelMonitoringSchema, ModelMonitoringSchema.Builder
@Generated public interface ModelMonitoringSchemaOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • getFeatureFieldsList

      List<ModelMonitoringSchema.FieldSchema> getFeatureFieldsList()
       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][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.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].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;

    • getFeatureFields

      ModelMonitoringSchema.FieldSchema getFeatureFields(int index)
       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][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.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].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;

    • getFeatureFieldsCount

      int getFeatureFieldsCount()
       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][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.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].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;

    • getFeatureFieldsOrBuilderList

      List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getFeatureFieldsOrBuilderList()
       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][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.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].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;

    • getFeatureFieldsOrBuilder

      ModelMonitoringSchema.FieldSchemaOrBuilder getFeatureFieldsOrBuilder(int index)
       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][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.instance_type]
 is an array, ensure that the sequence in
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.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].
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema feature_fields = 1;

    • getPredictionFieldsList

      List<ModelMonitoringSchema.FieldSchema> getPredictionFieldsList()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_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.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;

    • getPredictionFields

      ModelMonitoringSchema.FieldSchema getPredictionFields(int index)
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_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.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;

    • getPredictionFieldsCount

      int getPredictionFieldsCount()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_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.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;

    • getPredictionFieldsOrBuilderList

      List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getPredictionFieldsOrBuilderList()
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_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.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;

    • getPredictionFieldsOrBuilder

      ModelMonitoringSchema.FieldSchemaOrBuilder getPredictionFieldsOrBuilder(int index)
       Prediction output names of the model. The requirements are the same as the
 [feature_fields][google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.feature_fields].
 For AutoML Tables, the prediction output name presented in schema will be:
 `predicted_{target_column}`, the `target_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.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema prediction_fields = 2;

    • getGroundTruthFieldsList

      List<ModelMonitoringSchema.FieldSchema> getGroundTruthFieldsList()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • getGroundTruthFields

      ModelMonitoringSchema.FieldSchema getGroundTruthFields(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • getGroundTruthFieldsCount

      int getGroundTruthFieldsCount()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • getGroundTruthFieldsOrBuilderList

      List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getGroundTruthFieldsOrBuilderList()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • getGroundTruthFieldsOrBuilder

      ModelMonitoringSchema.FieldSchemaOrBuilder getGroundTruthFieldsOrBuilder(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;