← Back

BigQuery ML → Train & Predict

Build and serve a churn model in pure SQL with BigQuery ML—train, evaluate, predict, and schedule retrains.

bigquery-ml-train-predict

Overview

You’ll aggregate transactional data to craft features (e.g., recency/frequency/monetary, tenure, support tickets), then train a BQML model via `CREATE MODEL`. Use `ML.EVALUATE` to review metrics and `ML.PREDICT` to generate churn probabilities into a downstream table. A scheduled query (or Composer) retrains and re-scores periodically, keeping predictions fresh for BI or activation.

Outcome

  • Warehouse-native ML: no data movement, fully in BigQuery.
  • Actionable predictions (churn likelihood) materialized to tables.
  • Operationalized ML with scheduled retrains and accuracy tracking.

What you’ll build

  • A curated features table (SQL aggregations over transactions).
  • `CREATE MODEL` (e.g., logistic_reg or autoML).
  • Evaluation queries using ML.EVALUATE (AUC, accuracy, precision/recall).
  • Batch scoring with ML.PREDICT to a predictions table.
  • A scheduled query / Composer DAG to retrain + rescore on cadence.
Join the waitlist