generates predictions for an entire table of unlabeled data
and saves the results to an output table. Predictions are
performed in parallel.
is a compute intensive process. Limiting operations to batches
of 10 to 100 rows by splitting large tables into smaller
tables is recommended.
CALL sys.ML_PREDICT_TABLE(table_name, model_handle, output_table_name);
table_name: Specifies the fully qualified name of the input table (
schema_name.table_name). The input table should contain the same feature columns as the training dataset but no target column.
model_handle: Specifies the model handle or a session variable containing the model handle
output_table_name: Specifies the table where predictions are stored. The table is created if it does not exist. A fully qualified table name must be specified (
schema_name.table_name). If the table already exists, an error is returned.
A typical usage example that specifies the fully qualified name of the table to generate predictions for, the session variable containing the model handle, and the fully qualified output table name:
CALL sys.ML_PREDICT_TABLE('ml_data.iris_test', @iris_model, 'ml_data.iris_predictions');