Explanation:
Option B is the correct answer because it implements an efficient, event-driven retraining process that triggers model updates only when necessary - specifically when code changes occur (via Git repository trigger in Cloud Build) or when new data becomes available (via Pub/Sub trigger from Cloud Storage). This approach avoids unnecessary retraining costs and ensures the model stays current with both code and data changes. Option A only addresses data drift but ignores code changes. Options C and D use fixed schedules (daily/weekly) which are inefficient as they retrain regardless of whether changes have occurred, potentially wasting resources when no updates are needed.
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You have deployed an image classification model on Google Cloud using a Cloud Build CI/CD pipeline. How can you implement an efficient retraining process to keep the model current with data and code changes?
A
Use Cloud Run functions to monitor data drift in real time and trigger a Vertex AI Training job to retrain the model when data drift exceeds a predetermined threshold.
B
Configure a Git repository trigger in Cloud Build to initiate retraining when there are new code commits to the model's repository and a Pub/Sub trigger when there is new data in Cloud Storage.
C
Use Cloud Scheduler to initiate a daily retraining job in Vertex AI Pipelines.
D
Configure Cloud Composer to orchestrate a weekly retraining job that includes data extraction from BigQuery, model retraining with Vertex AI Training, and model deployment to a Vertex AI endpoint.