Google Professional Machine Learning Engineer
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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.
Explanation:
The correct sequence involves first deploying the model in a test environment to ensure it operates as expected under controlled conditions, followed by thorough evaluation and testing to confirm it meets all performance and compliance criteria. Only after these steps should a new AI Platform model version be created, ensuring that only validated and tested models are versioned and considered for production deployment. This approach minimizes risks and ensures scalability and compliance. Option E also presents a valid sequence, emphasizing the importance of evaluation and testing before any versioning or deployment, making it a correct alternative when considering two options.