Question

A retail analytics team stores millions of customer feedback comments in a BigQuery table. Analysts who know SQL but have no ML or Python experience want to classify each comment into one of five predefined product-category themes and generate a one-sentence summary, running the process entirely within their existing BigQuery workflows. They want the fastest path to production without training a custom model or standing up separate inference infrastructure. What should they do?

Accepted Answer

Create a BigQuery ML remote model that references a Vertex AI Gemini endpoint, then call ML.GENERATE_TEXT with a prompt column that instructs the model to classify and summarize each comment.

Answer

Export the comments to Cloud Storage, train an AutoML text classification model in Vertex AI, deploy it to an online endpoint, and call it from a scheduled query.

Answer

Use CREATE MODEL with model_type='logistic_reg' in BigQuery ML to train a multiclass classifier directly on the raw comment text.

Answer

Build a Vertex AI Pipeline that reads from BigQuery, fine-tunes a foundation model on the comments, and writes predictions back to a results table.