Topic 1 Question 234
You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions. Recently you developed a new version of the model that uses a different architecture (custom model). Initial analysis revealed that both models are performing as expected. You want to deploy the new version of the model to production and monitor the performance over the next two months. You need to minimize the impact to the existing and future model users. How should you deploy the model?
Import the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
Import the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
Import the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.
Deploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.
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コメント(6)
- 正解だと思う選択肢: A
A, no need to separate endpoint.
👍 4Yan_X2024/01/28 - 正解だと思う選択肢: A
Minimal Disruption: Deploying the new model to the same endpoint avoids changes for existing users. Traffic splitting ensures a gradual rollout, minimizing any potential impact on production. Performance Monitoring: By routing a small percentage of traffic (5%) to the new model, you can monitor its performance in a controlled environment for the next two months. Metrics like prediction accuracy and latency can be compared with the BigQuery ML model. Versioning in Model Registry: Storing both models in the same Vertex AI Model Registry with clear versioning allows easy tracking and management.
👍 4fitri0012024/04/17 - 正解だと思う選択肢: A
B. Doesn't Specify Traffic Splitting: Deploying models to a single endpoint without explicit traffic splitting might lead to unpredictable model selection behavior, hindering controlled evaluation. C. Separate Endpoints: While isolating models, it introduces complexity in managing multiple endpoints and routing logic, increasing operational overhead. D. Cloud Run Routing: Adds complexity by requiring a separate service to manage routing, potentially increasing latency and maintenance overhead compared to Vertex AI's built-in traffic splitting.
👍 2pikachu0072024/01/12
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