ML.DESCRIBE_DATA:
compute descriptive statistics for a set of training or serving data.
ML.VALIDATE_DATA_SKEW:
compute the statistics for a set of serving data, and then compare them to
the statistics for the data used to train a BigQuery ML model in order to
identify anomalous differences between the two data sets.
ML.VALIDATE_DATA_DRIFT:
compute and compare the statistics for two sets of serving data in order to
identify anomalous differences between the two data sets.
ML.TFDV_VALIDATE:
compute and compare the statistics for training and serving data, or two
sets of serving data, in order to identify anomalous differences between
the two data sets. This function provides the same behavior as the
TensorFlow tfdv.validate_statistics API.
Feature
You can perform model monitoring in BigQuery ML. The following model monitoring functions are now generally available (GA):
ML.DESCRIBE_DATA: compute descriptive statistics for a set of training or serving data.ML.VALIDATE_DATA_SKEW: compute the statistics for a set of serving data, and then compare them to the statistics for the data used to train a BigQuery ML model in order to identify anomalous differences between the two data sets.ML.VALIDATE_DATA_DRIFT: compute and compare the statistics for two sets of serving data in order to identify anomalous differences between the two data sets.ML.TFDV_DESCRIBE: compute fine-grained descriptive statistics for a set of training or serving data. This function provides the same behavior as the TensorFlowtfdv.generate_statistics_from_csvAPI.ML.TFDV_VALIDATE: compute and compare the statistics for training and serving data, or two sets of serving data, in order to identify anomalous differences between the two data sets. This function provides the same behavior as the TensorFlowtfdv.validate_statisticsAPI.https://cloud.google.com/bigquery/docs/release-notes#September_19_2024