Package com.google.cloud.aiplatform.v1beta1

Class ModelMonitoringSchema.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder>
com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.Builder
All Implemented Interfaces:
ModelMonitoringSchemaOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
ModelMonitoringSchema
public static final class ModelMonitoringSchema.Builder extends com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder> implements ModelMonitoringSchemaOrBuilder
 The Model Monitoring Schema definition.
Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringSchema

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder>

    • clear

      public ModelMonitoringSchema.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder>

    • getDefaultInstanceForType

      public ModelMonitoringSchema getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public ModelMonitoringSchema build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public ModelMonitoringSchema buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public ModelMonitoringSchema.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ModelMonitoringSchema.Builder>

    • mergeFrom

      public ModelMonitoringSchema.Builder mergeFrom(ModelMonitoringSchema other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<ModelMonitoringSchema.Builder>

    • mergeFrom

      public ModelMonitoringSchema.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ModelMonitoringSchema.Builder>
      Throws:
      IOException

    • getFeatureFieldsList

      public 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;
      Specified by:
      getFeatureFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFieldsCount

      public 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;
      Specified by:
      getFeatureFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFields

      public 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;
      Specified by:
      getFeatureFields in interface ModelMonitoringSchemaOrBuilder

    • setFeatureFields

      public ModelMonitoringSchema.Builder setFeatureFields(int index, ModelMonitoringSchema.FieldSchema value)
       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;

    • setFeatureFields

      public ModelMonitoringSchema.Builder setFeatureFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addFeatureFields

      public ModelMonitoringSchema.Builder addFeatureFields(ModelMonitoringSchema.FieldSchema value)
       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;

    • addFeatureFields

      public ModelMonitoringSchema.Builder addFeatureFields(int index, ModelMonitoringSchema.FieldSchema value)
       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;

    • addFeatureFields

      public ModelMonitoringSchema.Builder addFeatureFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addFeatureFields

      public ModelMonitoringSchema.Builder addFeatureFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addAllFeatureFields

      public ModelMonitoringSchema.Builder addAllFeatureFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values)
       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;

    • clearFeatureFields

      public ModelMonitoringSchema.Builder clearFeatureFields()
       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;

    • removeFeatureFields

      public ModelMonitoringSchema.Builder removeFeatureFields(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;

    • getFeatureFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder getFeatureFieldsBuilder(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;

    • getFeatureFieldsOrBuilder

      public 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;
      Specified by:
      getFeatureFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • getFeatureFieldsOrBuilderList

      public 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;
      Specified by:
      getFeatureFieldsOrBuilderList in interface ModelMonitoringSchemaOrBuilder

    • addFeatureFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder addFeatureFieldsBuilder()
       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;

    • addFeatureFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder addFeatureFieldsBuilder(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;

    • getFeatureFieldsBuilderList

      public List<ModelMonitoringSchema.FieldSchema.Builder> getFeatureFieldsBuilderList()
       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

      public 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;
      Specified by:
      getPredictionFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsCount

      public 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;
      Specified by:
      getPredictionFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFields

      public 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;
      Specified by:
      getPredictionFields in interface ModelMonitoringSchemaOrBuilder

    • setPredictionFields

      public ModelMonitoringSchema.Builder setPredictionFields(int index, ModelMonitoringSchema.FieldSchema value)
       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;

    • setPredictionFields

      public ModelMonitoringSchema.Builder setPredictionFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addPredictionFields

      public ModelMonitoringSchema.Builder addPredictionFields(ModelMonitoringSchema.FieldSchema value)
       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;

    • addPredictionFields

      public ModelMonitoringSchema.Builder addPredictionFields(int index, ModelMonitoringSchema.FieldSchema value)
       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;

    • addPredictionFields

      public ModelMonitoringSchema.Builder addPredictionFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addPredictionFields

      public ModelMonitoringSchema.Builder addPredictionFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       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;

    • addAllPredictionFields

      public ModelMonitoringSchema.Builder addAllPredictionFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values)
       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;

    • clearPredictionFields

      public ModelMonitoringSchema.Builder clearPredictionFields()
       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;

    • removePredictionFields

      public ModelMonitoringSchema.Builder removePredictionFields(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;

    • getPredictionFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder getPredictionFieldsBuilder(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;

    • getPredictionFieldsOrBuilder

      public 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;
      Specified by:
      getPredictionFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • getPredictionFieldsOrBuilderList

      public 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;
      Specified by:
      getPredictionFieldsOrBuilderList in interface ModelMonitoringSchemaOrBuilder

    • addPredictionFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder addPredictionFieldsBuilder()
       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;

    • addPredictionFieldsBuilder

      public ModelMonitoringSchema.FieldSchema.Builder addPredictionFieldsBuilder(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;

    • getPredictionFieldsBuilderList

      public List<ModelMonitoringSchema.FieldSchema.Builder> getPredictionFieldsBuilderList()
       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

      public List<ModelMonitoringSchema.FieldSchema> getGroundTruthFieldsList()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsList in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsCount

      public int getGroundTruthFieldsCount()
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsCount in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFields

      public ModelMonitoringSchema.FieldSchema getGroundTruthFields(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFields in interface ModelMonitoringSchemaOrBuilder

    • setGroundTruthFields

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

    • setGroundTruthFields

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

    • addGroundTruthFields

      public ModelMonitoringSchema.Builder addGroundTruthFields(ModelMonitoringSchema.FieldSchema value)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • addGroundTruthFields

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

    • addGroundTruthFields

      public ModelMonitoringSchema.Builder addGroundTruthFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • addGroundTruthFields

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

    • addAllGroundTruthFields

      public ModelMonitoringSchema.Builder addAllGroundTruthFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

    • clearGroundTruthFields

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

    • removeGroundTruthFields

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

    • getGroundTruthFieldsBuilder

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

    • getGroundTruthFieldsOrBuilder

      public ModelMonitoringSchema.FieldSchemaOrBuilder getGroundTruthFieldsOrBuilder(int index)
       Target /ground truth names of the model.
      repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;
      Specified by:
      getGroundTruthFieldsOrBuilder in interface ModelMonitoringSchemaOrBuilder

    • getGroundTruthFieldsOrBuilderList

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

    • addGroundTruthFieldsBuilder

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

    • addGroundTruthFieldsBuilder

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

    • getGroundTruthFieldsBuilderList

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