Struct google_api_proto::google::cloud::aiplatform::v1beta1::ModelMonitoringSchema

pub struct ModelMonitoringSchema {
    pub feature_fields: Vec<FieldSchema>,
    pub prediction_fields: Vec<FieldSchema>,
    pub ground_truth_fields: Vec<FieldSchema>,
}

The Model Monitoring Schema definition.

Fields

feature_fields: Vec<FieldSchema>

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].

prediction_fields: Vec<FieldSchema>

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.

ground_truth_fields: Vec<FieldSchema>

Target /ground truth names of the model.

Trait Implementations

impl Clone for ModelMonitoringSchema

fn clone(&self) -> ModelMonitoringSchema

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ModelMonitoringSchema

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ModelMonitoringSchema

fn default() -> Self

Returns the “default value” for a type.

impl Message for ModelMonitoringSchema

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ModelMonitoringSchema

fn eq(&self, other: &ModelMonitoringSchema) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ModelMonitoringSchema