Class ModelMonitoringSchema (3.53.0)

public final class ModelMonitoringSchema extends GeneratedMessageV3 implements ModelMonitoringSchemaOrBuilder

The Model Monitoring Schema definition.

Protobuf type google.cloud.aiplatform.v1beta1.ModelMonitoringSchema

Static Fields

FEATURE_FIELDS_FIELD_NUMBER

public static final int FEATURE_FIELDS_FIELD_NUMBER
Field Value
Type Description
int

GROUND_TRUTH_FIELDS_FIELD_NUMBER

public static final int GROUND_TRUTH_FIELDS_FIELD_NUMBER
Field Value
Type Description
int

PREDICTION_FIELDS_FIELD_NUMBER

public static final int PREDICTION_FIELDS_FIELD_NUMBER
Field Value
Type Description
int

Static Methods

getDefaultInstance()

public static ModelMonitoringSchema getDefaultInstance()
Returns
Type Description
ModelMonitoringSchema

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
Type Description
Descriptor

newBuilder()

public static ModelMonitoringSchema.Builder newBuilder()
Returns
Type Description
ModelMonitoringSchema.Builder

newBuilder(ModelMonitoringSchema prototype)

public static ModelMonitoringSchema.Builder newBuilder(ModelMonitoringSchema prototype)
Parameter
Name Description
prototype ModelMonitoringSchema
Returns
Type Description
ModelMonitoringSchema.Builder

parseDelimitedFrom(InputStream input)

public static ModelMonitoringSchema parseDelimitedFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseFrom(byte[] data)

public static ModelMonitoringSchema parseFrom(byte[] data)
Parameter
Name Description
data byte[]
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data byte[]
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data)

public static ModelMonitoringSchema parseFrom(ByteString data)
Parameter
Name Description
data ByteString
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteString
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static ModelMonitoringSchema parseFrom(CodedInputStream input)
Parameter
Name Description
input CodedInputStream
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input CodedInputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseFrom(InputStream input)

public static ModelMonitoringSchema parseFrom(InputStream input)
Parameter
Name Description
input InputStream
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
input InputStream
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
IOException

parseFrom(ByteBuffer data)

public static ModelMonitoringSchema parseFrom(ByteBuffer data)
Parameter
Name Description
data ByteBuffer
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringSchema parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
Name Description
data ByteBuffer
extensionRegistry ExtensionRegistryLite
Returns
Type Description
ModelMonitoringSchema
Exceptions
Type Description
InvalidProtocolBufferException

parser()

public static Parser<ModelMonitoringSchema> parser()
Returns
Type Description
Parser<ModelMonitoringSchema>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
Name Description
obj Object
Returns
Type Description
boolean
Overrides

getDefaultInstanceForType()

public ModelMonitoringSchema getDefaultInstanceForType()
Returns
Type Description
ModelMonitoringSchema

getFeatureFields(int index)

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 is an array, ensure that the sequence in 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;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchema

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 is an array, ensure that the sequence in 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;

Returns
Type Description
int

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 is an array, ensure that the sequence in 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;

Returns
Type Description
List<FieldSchema>

getFeatureFieldsOrBuilder(int index)

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 is an array, ensure that the sequence in 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;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchemaOrBuilder

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 is an array, ensure that the sequence in 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;

Returns
Type Description
List<? extends com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchemaOrBuilder>

getGroundTruthFields(int index)

public ModelMonitoringSchema.FieldSchema getGroundTruthFields(int index)

Target /ground truth names of the model.

repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchema

getGroundTruthFieldsCount()

public int getGroundTruthFieldsCount()

Target /ground truth names of the model.

repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

Returns
Type Description
int

getGroundTruthFieldsList()

public List<ModelMonitoringSchema.FieldSchema> getGroundTruthFieldsList()

Target /ground truth names of the model.

repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

Returns
Type Description
List<FieldSchema>

getGroundTruthFieldsOrBuilder(int index)

public ModelMonitoringSchema.FieldSchemaOrBuilder getGroundTruthFieldsOrBuilder(int index)

Target /ground truth names of the model.

repeated .google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchema ground_truth_fields = 3;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchemaOrBuilder

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;

Returns
Type Description
List<? extends com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchemaOrBuilder>

getParserForType()

public Parser<ModelMonitoringSchema> getParserForType()
Returns
Type Description
Parser<ModelMonitoringSchema>
Overrides

getPredictionFields(int index)

public ModelMonitoringSchema.FieldSchema getPredictionFields(int index)

Prediction output names of the model. The requirements are the same as the 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;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchema

getPredictionFieldsCount()

public int getPredictionFieldsCount()

Prediction output names of the model. The requirements are the same as the 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;

Returns
Type Description
int

getPredictionFieldsList()

public List<ModelMonitoringSchema.FieldSchema> getPredictionFieldsList()

Prediction output names of the model. The requirements are the same as the 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;

Returns
Type Description
List<FieldSchema>

getPredictionFieldsOrBuilder(int index)

public ModelMonitoringSchema.FieldSchemaOrBuilder getPredictionFieldsOrBuilder(int index)

Prediction output names of the model. The requirements are the same as the 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;

Parameter
Name Description
index int
Returns
Type Description
ModelMonitoringSchema.FieldSchemaOrBuilder

getPredictionFieldsOrBuilderList()

public List<? extends ModelMonitoringSchema.FieldSchemaOrBuilder> getPredictionFieldsOrBuilderList()

Prediction output names of the model. The requirements are the same as the 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;

Returns
Type Description
List<? extends com.google.cloud.aiplatform.v1beta1.ModelMonitoringSchema.FieldSchemaOrBuilder>

getSerializedSize()

public int getSerializedSize()
Returns
Type Description
int
Overrides

hashCode()

public int hashCode()
Returns
Type Description
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Type Description
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
Type Description
boolean
Overrides

newBuilderForType()

public ModelMonitoringSchema.Builder newBuilderForType()
Returns
Type Description
ModelMonitoringSchema.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected ModelMonitoringSchema.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
Name Description
parent BuilderParent
Returns
Type Description
ModelMonitoringSchema.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
Name Description
unused UnusedPrivateParameter
Returns
Type Description
Object
Overrides

toBuilder()

public ModelMonitoringSchema.Builder toBuilder()
Returns
Type Description
ModelMonitoringSchema.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
Name Description
output CodedOutputStream
Overrides
Exceptions
Type Description
IOException