Class ModelMonitoringSchema (1.69.0)

ModelMonitoringSchema(
    feature_fields: typing.MutableSequence[
        vertexai.resources.preview.ml_monitoring.spec.schema.FieldSchema
    ],
    ground_truth_fields: typing.Optional[
        typing.MutableSequence[
            vertexai.resources.preview.ml_monitoring.spec.schema.FieldSchema
        ]
    ] = None,
    prediction_fields: typing.Optional[
        typing.MutableSequence[
            vertexai.resources.preview.ml_monitoring.spec.schema.FieldSchema
        ]
    ] = None,
)

Initializer for ModelMonitoringSchema.

Parameters
Name	Description
feature_fields	MutableSequence[FieldSchema] Required. 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 thecorresponding 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 prediction instance format 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.
prediction_fields	MutableSequence[FieldSchema] Optional. 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.
ground_truth_fields	MutableSequence[FieldSchema] Optional. Target /ground truth names of the model.

Methods

to_json

to_json(output_dir: typing.Optional[str] = None) -> str

Transform ModelMonitoringSchema to json format.

Parameter
Name	Description
output_dir	str Optional. The output directory that the transformed json file would be put into.