In your role as a Machine Learning Engineer, your team utilizes Kubeflow pipelines for training various model versions, with each new model artifact stored in a Cloud Storage bucket. The subsequent step involves preparing these models for testing and eventual production deployment on AI Platform. Considering the need for cost-efficiency, compliance with data governance policies, and scalability for high-traffic applications, what is the optimal sequence of actions to configure the architecture before deploying the model to production? Choose the best option.