Question

A retail analytics team uses BigQuery to store transaction data. A data scientist wants to build a churn prediction model using Vertex AI's custom training with scikit-learn, but the raw features live in BigQuery. The team needs to generate a training dataset where numeric columns are standardized and categorical columns are one-hot encoded, and they want this preprocessing logic to be reusable and version-controlled outside of BigQuery ML. What is the most appropriate approach to prepare this data for AI/ML?

Accepted Answer

Export the raw BigQuery table to Cloud Storage as CSV and apply all preprocessing in the Vertex AI training container using a scikit-learn Pipeline, reading the data via the BigQuery Storage Read API.

Answer

Create a BigQuery ML model with a TRANSFORM clause to standardize and encode features, then export the model for use in Vertex AI.

Answer

Use Connected Sheets to pivot and normalize the data, then download the sheet as input for Vertex AI training.

Answer

Build a Dataflow streaming pipeline that reads from BigQuery, applies preprocessing, and writes to Pub/Sub for Vertex AI to consume.