You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will

Prophet on Vertex AI Training would require setting up a custom training job and managing code, which is more involved than BigQuery ML.

Vertex AI Forecast is a managed service, but BigQuery ML provides a more direct and minimal effort solution when data is already in BigQuery and SQL can be used.

BigQuery ML allows users to create and execute machine learning models directly within BigQuery using SQL queries, significantly reducing the effort and time required for model development and deployment. `ARIMA_PLUS` is a powerful statistical model specifically designed for time series forecasting, making it suitable for this use case.

Using TensorFlow on Vertex AI Training to build a custom model provides maximum flexibility but requires significant development effort, not aligning with the 'minimal effort' requirement.