Question

A data science team trained a boosted tree classifier using BigQuery ML to predict customer churn. Batch scoring in BigQuery works well for their nightly reports, but the product team now needs sub-100ms online predictions served from a customer-facing web application. What is the most appropriate way to meet this low-latency serving requirement while reusing the already-trained model?

Accepted Answer

Export the BigQuery ML model to Cloud Storage and deploy it to a Vertex AI endpoint for online prediction.

Answer

Run ML.PREDICT queries against BigQuery from the web application for each incoming request.

Answer

Schedule a materialized view in BigQuery that precomputes predictions for every possible customer state.

Answer

Use BigQuery BI Engine to accelerate ML.PREDICT so the query returns within 100ms.