Exam Professional Machine Learning Engineer topic 1 question 266 discussion

For Simplicity: If speed and simplicity are your top priorities, deploying to the existing endpoint with caution (close monitoring during deployment) can work.--> choose C For Safety and Control: If minimizing risk and having better control over the testing process are more important, creating a new endpoint is the better option. This is generally the recommended approach for most production deployments. --> choose A

Here's why the option C is preferable: Minimized complexity: Leverages existing endpoint: No need to create and manage a new endpoint, reducing setup and maintenance overhead. Traffic splitting readily available: Vertex AI provides built-in traffic splitting functionality, simplifying traffic distribution. Efficient testing and monitoring: Direct comparison: Sending a percentage of traffic to the new model allows for direct comparison with the current model's performance on real user data. Gradual rollout: Starting with a small percentage mitigates potential risks and allows for gradual transition based on observed improvements. End-user metric monitoring: Focusing on metrics like listening time directly reflects user engagement and preference for the new recommendations.

Traffic splitting is a feature of Vertex AI that allows you to distribute the prediction requests among multiple models or model versions within the same endpoint. You can specify the percentage of traffic that each model or model version receives, and change it at any time. Traffic splitting can help you test the new model in production without creating a new endpoint or a separate service. You can deploy the new model to the existing Vertex AI endpoint, and use traffic splitting to send 5% of production traffic to the new model. You can monitor the end-user metrics, such as listening time, to compare the performance of the new model and the previous model. If the end-user metrics improve between models over time, you can gradually increase the percentage of production traffic sent to the new model. This solution can help you test the new model in production while minimizing complexity and cost.

Option A: Building a separate service adds unnecessary complexity and requires managing two endpoints. Option B: Batch predictions in Vertex AI Experiments might not reflect real-time user behavior and don't directly affect the production environment. Option D: Model monitoring alerts for prediction drift might be triggered by natural variations in user behavior instead of genuine performance issues and could lead to unnecessary model rollbacks.