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Answer: Use BigQuery’s scheduling service to run the model retraining query periodically.
Option A is the optimal choice because it directly uses BigQuery's built-in scheduling service to run the model retraining query periodically. This approach minimizes development effort as it requires no additional setup, custom code, or management of external dependencies like pipelines, functions, or workflows. It also reduces scheduling costs by leveraging BigQuery's native capabilities without incurring extra charges for services like Vertex AI Pipelines, Cloud Functions, or Workflows. The community discussion strongly supports this, with 100% consensus and upvoted comments highlighting the simplicity and cost-effectiveness of BigQuery scheduling compared to the complexity and overhead of options B, C, and D.
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You have a linear regression model built with BigQuery ML that you need to retrain weekly on the cumulative data. Your goal is to minimize both development effort and scheduling overhead. What is your recommended approach?
A
Use BigQuery’s scheduling service to run the model retraining query periodically.
B
Create a pipeline in Vertex AI Pipelines that executes the retraining query, and use the Cloud Scheduler API to run the query weekly.
C
Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
D
Use the BigQuery API Connector and Cloud Scheduler to trigger Workflows every week that retrains the model.