Question

A retail analytics team has an existing TensorFlow SavedModel that predicts product return likelihood. The model was trained outside Google Cloud by a data science partner. The team wants their SQL-focused analysts to run batch predictions on customer transaction data that already lives in BigQuery, without exporting the data or standing up a separate serving infrastructure. What is the most appropriate low-code approach?

Accepted Answer

Use CREATE MODEL with the MODEL_TYPE='TENSORFLOW' option and MODEL_PATH pointing to the SavedModel in Cloud Storage, then run ML.PREDICT against the BigQuery table

Answer

Deploy the SavedModel to a Vertex AI endpoint and have analysts call the endpoint from a Cloud Function for each transaction row

Answer

Retrain the model from scratch as a BigQuery ML boosted tree classifier since imported TensorFlow models cannot be used for prediction in BigQuery

Answer

Export the BigQuery table to Cloud Storage as CSV and run the TensorFlow model locally with a Python batch script