An e-commerce retailer keeps millions of historical transaction records in BigQuery. The marketing team wants to quickly build a model that predicts whether a customer will make another purchase, and they have very limited machine-learning expertise. To avoid moving data out of BigQuery, which Google Cloud approach will deliver the needed insights with the least development effort and fastest time-to-value?
Export the data to Vertex AI and code a custom TensorFlow model that trains on Cloud TPUs.
Create an AutoML Vision model to perform image classification on the transaction tables.
Call the pre-trained Natural Language API to classify the transaction data.
Use BigQuery ML to train a classification model directly within BigQuery using SQL.
BigQuery ML lets users create and execute machine-learning models by writing standard SQL in the BigQuery environment, so data never leaves the warehouse. Logistic regression or other classification algorithms can be trained on the existing transaction table in just a few queries, requiring minimal ML knowledge and no separate infrastructure. Building a custom TensorFlow model on Vertex AI would demand far more time, skills, and data movement. The Natural Language API and AutoML Vision target unstructured text or images, not structured purchase records, so they would not meet the requirement.
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