You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes…

Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then

leveraging BigQueryML to train the churn prediction model is the Google-recommended approach

for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery

using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes

overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of

building and training the churn prediction model.