Question

A SaaS company stores millions of historical customer support tickets in BigQuery, each labeled with one of five routing categories (Billing, Technical, Account, Sales, Other). The data engineering team wants to automatically predict the routing category for new tickets using the text description column, and they prefer to build and serve the model without moving data out of BigQuery or writing custom training code. Which approach best meets these requirements?

Accepted Answer

Use BigQuery ML to train a model with MODEL_TYPE='LOGISTIC_REG' on the label column, letting BigQuery ML automatically detect the multiclass target and generate text feature transformations.

Answer

Create a BigQuery ML logistic regression model with MODEL_TYPE='LOGISTIC_REG', which natively handles the five-category target and text features.

Answer

Export the tickets to Cloud Storage and train a custom PyTorch text classifier on Vertex AI, then deploy it to an online endpoint.

Answer

Call the Cloud Natural Language API's classifyText method on each ticket to obtain the routing category directly.