A retail analytics team already stores purchase history in BigQuery and knows SQL. They want to build and run a predictive model for customer churn without moving data or managing ML infrastructure. Which Google Cloud capability best meets their needs?
Create the model with BigQuery ML using SQL queries
Call the Cloud Translation API
Build a custom TensorFlow model on Vertex AI Workbench and deploy it on Cloud TPU
BigQuery ML is designed to let analysts build, train, and execute machine-learning models directly inside BigQuery by writing familiar SQL statements. Because the data stays in the warehouse, the team avoids exporting datasets and does not have to provision or manage separate ML infrastructure. AutoML Vision focuses on image classification, Cloud Translation API handles language translation, and building a custom TensorFlow model on Vertex AI requires considerably more setup and ML expertise than the team needs for this use case.
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