Question

A retail analytics team stores 4 million product photos in Cloud Storage, with their metadata (GCS URIs, SKU, category) already loaded into a BigQuery object table. Analysts who are fluent in SQL but have no ML training pipeline experience need to generate descriptive labels for each image to enrich the product catalog. They want the lowest-effort approach that keeps everything within their existing BigQuery workflow and does not require them to build or manage any serving infrastructure. What should they do?

Accepted Answer

Create a BigQuery ML remote model that connects to the Cloud Vision API and call ML.ANNOTATE_IMAGE over the object table to generate labels directly in SQL.

Answer

Train an AutoML image classification model in Vertex AI, deploy it to an online endpoint, and have analysts write SQL that calls the endpoint per row.

Answer

Export all images to a Vertex AI managed dataset, run a custom TensorFlow training job to build a labeler, then batch predict and load results back to BigQuery.

Answer

Write a Dataflow pipeline that reads the object table, calls the Vision API for each image, and streams annotations into a BigQuery results table.