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 Summary

Modifier and Type	Method and Description
ModelMonitoringSchema.Builder	addAllFeatureFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values) Feature names of the model.
ModelMonitoringSchema.Builder	addAllGroundTruthFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	addAllPredictionFields(Iterable<? extends ModelMonitoringSchema.FieldSchema> values) Prediction output names of the model.
ModelMonitoringSchema.Builder	addFeatureFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Feature names of the model.
ModelMonitoringSchema.Builder	addFeatureFields(int index, ModelMonitoringSchema.FieldSchema value) Feature names of the model.
ModelMonitoringSchema.Builder	addFeatureFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue) Feature names of the model.
ModelMonitoringSchema.Builder	addFeatureFields(ModelMonitoringSchema.FieldSchema value) Feature names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addFeatureFieldsBuilder() Feature names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addFeatureFieldsBuilder(int index) Feature names of the model.
ModelMonitoringSchema.Builder	addGroundTruthFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	addGroundTruthFields(int index, ModelMonitoringSchema.FieldSchema value) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	addGroundTruthFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	addGroundTruthFields(ModelMonitoringSchema.FieldSchema value) Target /ground truth names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addGroundTruthFieldsBuilder() Target /ground truth names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addGroundTruthFieldsBuilder(int index) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	addPredictionFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Prediction output names of the model.
ModelMonitoringSchema.Builder	addPredictionFields(int index, ModelMonitoringSchema.FieldSchema value) Prediction output names of the model.
ModelMonitoringSchema.Builder	addPredictionFields(ModelMonitoringSchema.FieldSchema.Builder builderForValue) Prediction output names of the model.
ModelMonitoringSchema.Builder	addPredictionFields(ModelMonitoringSchema.FieldSchema value) Prediction output names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addPredictionFieldsBuilder() Prediction output names of the model.
ModelMonitoringSchema.FieldSchema.Builder	addPredictionFieldsBuilder(int index) Prediction output names of the model.
ModelMonitoringSchema	build()
ModelMonitoringSchema	buildPartial()
ModelMonitoringSchema.Builder	clear()
ModelMonitoringSchema.Builder	clearFeatureFields() Feature names of the model.
ModelMonitoringSchema.Builder	clearGroundTruthFields() Target /ground truth names of the model.
ModelMonitoringSchema.Builder	clearPredictionFields() Prediction output names of the model.
ModelMonitoringSchema	getDefaultInstanceForType()
static com.google.protobuf.Descriptors.Descriptor	getDescriptor()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType()
ModelMonitoringSchema.FieldSchema	getFeatureFields(int index) Feature names of the model.
ModelMonitoringSchema.FieldSchema.Builder	getFeatureFieldsBuilder(int index) Feature names of the model.
List<ModelMonitoringSchema.FieldSchema.Builder>	getFeatureFieldsBuilderList() 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.
ModelMonitoringSchema.FieldSchema.Builder	getGroundTruthFieldsBuilder(int index) Target /ground truth names of the model.
List<ModelMonitoringSchema.FieldSchema.Builder>	getGroundTruthFieldsBuilderList() 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.
ModelMonitoringSchema.FieldSchema.Builder	getPredictionFieldsBuilder(int index) Prediction output names of the model.
List<ModelMonitoringSchema.FieldSchema.Builder>	getPredictionFieldsBuilderList() 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.
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable	internalGetFieldAccessorTable()
boolean	isInitialized()
ModelMonitoringSchema.Builder	mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
ModelMonitoringSchema.Builder	mergeFrom(com.google.protobuf.Message other)
ModelMonitoringSchema.Builder	mergeFrom(ModelMonitoringSchema other)
ModelMonitoringSchema.Builder	removeFeatureFields(int index) Feature names of the model.
ModelMonitoringSchema.Builder	removeGroundTruthFields(int index) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	removePredictionFields(int index) Prediction output names of the model.
ModelMonitoringSchema.Builder	setFeatureFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Feature names of the model.
ModelMonitoringSchema.Builder	setFeatureFields(int index, ModelMonitoringSchema.FieldSchema value) Feature names of the model.
ModelMonitoringSchema.Builder	setGroundTruthFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	setGroundTruthFields(int index, ModelMonitoringSchema.FieldSchema value) Target /ground truth names of the model.
ModelMonitoringSchema.Builder	setPredictionFields(int index, ModelMonitoringSchema.FieldSchema.Builder builderForValue) Prediction output names of the model.
ModelMonitoringSchema.Builder	setPredictionFields(int index, ModelMonitoringSchema.FieldSchema value) Prediction output names of the model.

Methods inherited from class com.google.protobuf.GeneratedMessage.Builder

addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

Methods inherited from interface com.google.protobuf.Message.Builder

mergeDelimitedFrom, mergeDelimitedFrom

Methods inherited from interface com.google.protobuf.MessageLite.Builder

mergeFrom

Method Detail

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;