Question

A retail analytics team stores millions of transaction records in BigQuery. A data scientist has already trained and deployed a custom fraud-scoring model to a Vertex AI online endpoint. The business analysts, who only know SQL, need to run predictions against this deployed model directly from their BigQuery queries without exporting data or writing Python. Which approach best meets this requirement with the least operational overhead?

Accepted Answer

Create a BigQuery ML remote model referencing the Vertex AI endpoint, then call ML.PREDICT on it from SQL.

Answer

Export the BigQuery table to Cloud Storage and run a Vertex AI batch prediction job against the endpoint.

Answer

Re-train the fraud model entirely inside BigQuery ML using CREATE MODEL so the analysts can use ML.PREDICT.

Answer

Have the analysts call the Vertex AI endpoint through a Cloud Function that they invoke via HTTP for each row.