Question

A retail analytics team stores all their customer transaction data in BigQuery. They want to build a churn prediction model directly in BigQuery ML using a boosted tree classifier, and they want BigQuery ML to automatically search for the best combination of learning rate and max tree depth rather than manually experimenting. They also need the final model to be usable for online predictions from a Vertex AI endpoint later. Which approach best meets these requirements with the least custom code?

Accepted Answer

Create a BOOSTED_TREE_CLASSIFIER model in BigQuery ML with NUM_TRIALS and HPARAM_RANGE/HPARAM_CANDIDATES options to enable hyperparameter tuning, then export the model and register it to Vertex AI for online serving.

Answer

Train a custom XGBoost model in a Vertex AI custom training job, manually tune hyperparameters with Vertex AI Vizier, then deploy to an endpoint.

Answer

Use AutoML Tables in Vertex AI, which handles hyperparameter tuning automatically, and skip BigQuery ML entirely.

Answer

Create a LOGISTIC_REG model in BigQuery ML with L1_REG and L2_REG regularization options, since boosted trees do not support hyperparameter tuning in BigQuery ML.