You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?
Using BigQuery’s scheduling service to run the model retraining query periodically minimizes development effort and scheduling cost. BigQuery offers a native scheduling feature that allows you to automate SQL queries without requiring additional services, custom code, or extensive configuration. This straightforward solution aligns perfectly with the requirement to reduce both development effort and scheduling costs.
No additional setup: BigQuery's scheduling feature is built-in, eliminating the need to create pipelines, functions, or workflows. Straightforward configuration: Setting up a schedule for a query is a simple process within the BigQuery interface.
Option B: Vertex AI Pipelines offer flexibility for complex workflows, but it involves more development effort and potential costs for pipeline execution. Option C: Cloud Functions provide a serverless way to execute code, but they incur execution costs and require additional configuration for triggering and permissions. Option D: Workflows can manage complex orchestration, but configuring the BigQuery API Connector and Cloud Scheduler adds complexity and potential costs.
No-brainer A.
A is right Using BigQuery's scheduling service allows you to automate the retraining process without needing to write custom code or manage additional dependencies.