com.google.cloud.aiplatform.v1beta1

Interface ModelMonitoringSchemaOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ModelMonitoringSchema, ModelMonitoringSchema.Builder

public interface ModelMonitoringSchemaOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
ModelMonitoringSchema.FieldSchema	getFeatureFields(int index) Feature names of the model.
int	getFeatureFieldsCount() Feature names of the model.
List<ModelMonitoringSchema.FieldSchema>	getFeatureFieldsList() Feature names of the model.
ModelMonitoringSchema.FieldSchemaOrBuilder	getFeatureFieldsOrBuilder(int index) Feature names of the model.
List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder>	getFeatureFieldsOrBuilderList() Feature names of the model.
ModelMonitoringSchema.FieldSchema	getGroundTruthFields(int index) Target /ground truth names of the model.
int	getGroundTruthFieldsCount() Target /ground truth names of the model.
List<ModelMonitoringSchema.FieldSchema>	getGroundTruthFieldsList() Target /ground truth names of the model.
ModelMonitoringSchema.FieldSchemaOrBuilder	getGroundTruthFieldsOrBuilder(int index) Target /ground truth names of the model.
List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder>	getGroundTruthFieldsOrBuilderList() Target /ground truth names of the model.
ModelMonitoringSchema.FieldSchema	getPredictionFields(int index) Prediction output names of the model.
int	getPredictionFieldsCount() Prediction output names of the model.
List<ModelMonitoringSchema.FieldSchema>	getPredictionFieldsList() Prediction output names of the model.
ModelMonitoringSchema.FieldSchemaOrBuilder	getPredictionFieldsOrBuilder(int index) Prediction output names of the model.
List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder>	getPredictionFieldsOrBuilderList() Prediction output names of the model.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

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;