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public interface ModelMonitoringSchemaOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
getFeatureFields(int index)
public abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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 abstract 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> |
|
getPredictionFields(int index)
public abstract 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 abstract 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 abstract 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 abstract 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 abstract 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> |
|