com.google.cloud.aiplatform.v1beta1

Class ModelMonitoringSchema

java.lang.Object

com.google.protobuf.AbstractMessageLite

com.google.protobuf.AbstractMessage

com.google.protobuf.GeneratedMessageV3

com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema

All Implemented Interfaces:

ModelMonitoringSchemaOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable

public final class ModelMonitoringSchema
extends com.google.protobuf.GeneratedMessageV3
implements ModelMonitoringSchemaOrBuilder

 The Model Monitoring Schema definition.
 

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringSchema

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ModelMonitoringSchema.Builder The Model Monitoring Schema definition.
static class	ModelMonitoringSchema.FieldSchema Schema field definition.
static interface	ModelMonitoringSchema.FieldSchemaOrBuilder

Nested classes/interfaces inherited from class com.google.protobuf.GeneratedMessageV3

com.google.protobuf.GeneratedMessageV3.BuilderParent, com.google.protobuf.GeneratedMessageV3.ExtendableBuilder<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>,BuilderT extends com.google.protobuf.GeneratedMessageV3.ExtendableBuilder<MessageT,BuilderT>>, com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessageV3.ExtendableMessageOrBuilder<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessageV3.FieldAccessorTable, com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter

Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessageLite

com.google.protobuf.AbstractMessageLite.InternalOneOfEnum

Field Summary

Fields

Modifier and Type	Field and Description
static int	FEATURE_FIELDS_FIELD_NUMBER
static int	GROUND_TRUTH_FIELDS_FIELD_NUMBER
static int	PREDICTION_FIELDS_FIELD_NUMBER

Fields inherited from class com.google.protobuf.GeneratedMessageV3

alwaysUseFieldBuilders, unknownFields

Fields inherited from class com.google.protobuf.AbstractMessage

memoizedSize

Fields inherited from class com.google.protobuf.AbstractMessageLite

memoizedHashCode

Method Summary

Modifier and Type	Method and Description
boolean	equals(Object obj)
static ModelMonitoringSchema	getDefaultInstance()
ModelMonitoringSchema	getDefaultInstanceForType()
static com.google.protobuf.Descriptors.Descriptor	getDescriptor()
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.
com.google.protobuf.Parser<ModelMonitoringSchema>	getParserForType()
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.
int	getSerializedSize()
int	hashCode()
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable	internalGetFieldAccessorTable()
boolean	isInitialized()
static ModelMonitoringSchema.Builder	newBuilder()
static ModelMonitoringSchema.Builder	newBuilder(ModelMonitoringSchema prototype)
ModelMonitoringSchema.Builder	newBuilderForType()
protected ModelMonitoringSchema.Builder	newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
protected Object	newInstance(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
static ModelMonitoringSchema	parseDelimitedFrom(InputStream input)
static ModelMonitoringSchema	parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ModelMonitoringSchema	parseFrom(byte[] data)
static ModelMonitoringSchema	parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ModelMonitoringSchema	parseFrom(ByteBuffer data)
static ModelMonitoringSchema	parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ModelMonitoringSchema	parseFrom(com.google.protobuf.ByteString data)
static ModelMonitoringSchema	parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ModelMonitoringSchema	parseFrom(com.google.protobuf.CodedInputStream input)
static ModelMonitoringSchema	parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static ModelMonitoringSchema	parseFrom(InputStream input)
static ModelMonitoringSchema	parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
static com.google.protobuf.Parser<ModelMonitoringSchema>	parser()
ModelMonitoringSchema.Builder	toBuilder()
void	writeTo(com.google.protobuf.CodedOutputStream output)

Methods inherited from class com.google.protobuf.GeneratedMessageV3

canUseUnsafe, computeStringSize, computeStringSizeNoTag, emptyBooleanList, emptyDoubleList, emptyFloatList, emptyIntList, emptyList, emptyLongList, getAllFields, getDescriptorForType, getField, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, isStringEmpty, makeExtensionsImmutable, makeMutableCopy, makeMutableCopy, mergeFromAndMakeImmutableInternal, mutableCopy, mutableCopy, mutableCopy, mutableCopy, mutableCopy, newBooleanList, newBuilderForType, newDoubleList, newFloatList, newIntList, newLongList, parseDelimitedWithIOException, parseDelimitedWithIOException, parseUnknownField, parseUnknownFieldProto3, parseWithIOException, parseWithIOException, parseWithIOException, parseWithIOException, serializeBooleanMapTo, serializeIntegerMapTo, serializeLongMapTo, serializeStringMapTo, writeReplace, writeString, writeStringNoTag

Methods inherited from class com.google.protobuf.AbstractMessage

findInitializationErrors, getInitializationErrorString, hashBoolean, hashEnum, hashEnumList, hashFields, hashLong, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite

addAll, addAll, checkByteStringIsUtf8, toByteArray, toByteString, writeDelimitedTo, writeTo

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder

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

Methods inherited from interface com.google.protobuf.MessageLite

toByteArray, toByteString, writeDelimitedTo, writeTo

Field Detail

FEATURE_FIELDS_FIELD_NUMBER

public static final int FEATURE_FIELDS_FIELD_NUMBER

See Also:

Constant Field Values

PREDICTION_FIELDS_FIELD_NUMBER

public static final int PREDICTION_FIELDS_FIELD_NUMBER

See Also:

Constant Field Values

GROUND_TRUTH_FIELDS_FIELD_NUMBER

public static final int GROUND_TRUTH_FIELDS_FIELD_NUMBER

See Also:

Constant Field Values

Method Detail

newInstance

protected Object newInstance(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)

Overrides:

newInstance in class com.google.protobuf.GeneratedMessageV3

getDescriptor

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

internalGetFieldAccessorTable

protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Specified by:

internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3

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

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

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

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

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

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

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

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

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

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

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

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

isInitialized

public final boolean isInitialized()

Specified by:

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

isInitialized in class com.google.protobuf.GeneratedMessageV3

writeTo

public void writeTo(com.google.protobuf.CodedOutputStream output)
             throws IOException

Specified by:

writeTo in interface com.google.protobuf.MessageLite

Overrides:

writeTo in class com.google.protobuf.GeneratedMessageV3

Throws:

IOException

getSerializedSize

public int getSerializedSize()

Specified by:

getSerializedSize in interface com.google.protobuf.MessageLite

Overrides:

getSerializedSize in class com.google.protobuf.GeneratedMessageV3

equals

public boolean equals(Object obj)

Specified by:

equals in interface com.google.protobuf.Message

Overrides:

equals in class com.google.protobuf.AbstractMessage

hashCode

public int hashCode()

Specified by:

hashCode in interface com.google.protobuf.Message

Overrides:

hashCode in class com.google.protobuf.AbstractMessage

parseFrom

public static ModelMonitoringSchema parseFrom(ByteBuffer data)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(ByteBuffer data,
                                              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(com.google.protobuf.ByteString data)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(com.google.protobuf.ByteString data,
                                              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(byte[] data)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(byte[] data,
                                              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                       throws com.google.protobuf.InvalidProtocolBufferException

Throws:

com.google.protobuf.InvalidProtocolBufferException

parseFrom

public static ModelMonitoringSchema parseFrom(InputStream input)
                                       throws IOException

Throws:

IOException

parseFrom

public static ModelMonitoringSchema parseFrom(InputStream input,
                                              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                       throws IOException

Throws:

IOException

parseDelimitedFrom

public static ModelMonitoringSchema parseDelimitedFrom(InputStream input)
                                                throws IOException

Throws:

IOException

parseDelimitedFrom

public static ModelMonitoringSchema parseDelimitedFrom(InputStream input,
                                                       com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                throws IOException

Throws:

IOException

parseFrom

public static ModelMonitoringSchema parseFrom(com.google.protobuf.CodedInputStream input)
                                       throws IOException

Throws:

IOException

parseFrom

public static ModelMonitoringSchema parseFrom(com.google.protobuf.CodedInputStream input,
                                              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                       throws IOException

Throws:

IOException

newBuilderForType

public ModelMonitoringSchema.Builder newBuilderForType()

Specified by:

newBuilderForType in interface com.google.protobuf.Message

Specified by:

newBuilderForType in interface com.google.protobuf.MessageLite

newBuilder

public static ModelMonitoringSchema.Builder newBuilder()

newBuilder

public static ModelMonitoringSchema.Builder newBuilder(ModelMonitoringSchema prototype)

toBuilder

public ModelMonitoringSchema.Builder toBuilder()

Specified by:

toBuilder in interface com.google.protobuf.Message

Specified by:

toBuilder in interface com.google.protobuf.MessageLite

newBuilderForType

protected ModelMonitoringSchema.Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)

Specified by:

newBuilderForType in class com.google.protobuf.GeneratedMessageV3

getDefaultInstance

public static ModelMonitoringSchema getDefaultInstance()

parser

public static com.google.protobuf.Parser<ModelMonitoringSchema> parser()

getParserForType

public com.google.protobuf.Parser<ModelMonitoringSchema> getParserForType()

Specified by:

getParserForType in interface com.google.protobuf.Message

Specified by:

getParserForType in interface com.google.protobuf.MessageLite

Overrides:

getParserForType in class com.google.protobuf.GeneratedMessageV3

getDefaultInstanceForType

public ModelMonitoringSchema getDefaultInstanceForType()

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder