Your team uses a CI/CD pipeline with Cloud Build to train and deploy ML models on Vertex AI. You want to ensure that only models that pass validation checks (e.g., accuracy threshold, fairness metrics) are promoted to production. What is the best way to implement this?
Validation scripts inside Cloud Build gate promotion on accuracy and fairness metrics, satisfying the stem's requirement that only models passing checks reach production. Staging with manual approval adds human review before promotion, though it introduces delay. This keeps enforcement within the existing pipeline rather than relying on post-deployment monitoring.
Why this answer
It integrates validation directly into the CI/CD pipeline using Cloud Build, ensuring that only models passing specific checks (e.g., accuracy threshold, fairness metrics) are promoted. By running validation scripts after training and requiring manual approval before production promotion, this approach provides both automated gatekeeping and human oversight, aligning with MLOps best practices for safe model deployment.
Exam trap
Google Cloud often tests the misconception that Vertex AI Model Registry has built-in automatic promotion based on evaluation metrics, but in reality, it requires external orchestration (like Cloud Build) to implement such logic.
How to eliminate wrong answers
Option A is wrong because Cloud Scheduler triggers retraining on a schedule, not based on validation results, and it does not integrate with the CI/CD pipeline to enforce promotion gates. Option B is wrong because Vertex AI Model Registry does not have an automatic promotion feature based on evaluation results; it stores and manages models but requires external logic to decide promotion. Option C is wrong because Cloud Functions re-evaluating the model daily is reactive and does not tie into the build pipeline's validation step, potentially promoting a model that was not validated at the time of training.