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Question 204 - Professional Machine Learning Engineer discussion

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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?

A.
Import the new model to the same Vertex Al Model Registry as a different version of the existing model. Deploy the new model to the same Vertex Al 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.
Answers
A.
Import the new model to the same Vertex Al Model Registry as a different version of the existing model. Deploy the new model to the same Vertex Al 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.
B.
Import the new model to the same Vertex Al Model Registry as the existing model Deploy the models to one Vertex Al endpoint Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model
Answers
B.
Import the new model to the same Vertex Al Model Registry as the existing model Deploy the models to one Vertex Al endpoint Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model
C.
Import the new model to the same Vertex Al Model Registry as the existing model Deploy each model to a separate Vertex Al endpoint.
Answers
C.
Import the new model to the same Vertex Al Model Registry as the existing model Deploy each model to a separate Vertex Al endpoint.
D.
Deploy the new model to a separate Vertex Al endpoint Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.
Answers
D.
Deploy the new model to a separate Vertex Al endpoint Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.
Suggested answer: A

Explanation:

Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models1.You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases1.You can also deploy models to endpoints, which are resources that provide a service URL for online prediction2.

By importing the new model to the same Vertex AI Model Registry as a different version of the existing model, you can keep track of the model versions and compare their performance metrics1.You can also use aliases to label the model versions according to their readiness for production, such asdefaultorstaging1.

By deploying the new model to the same Vertex AI endpoint as the existing model, you can use traffic splitting to gradually shift the production traffic from the old model to the new model2.Traffic splitting is a feature that allows you to specify the percentage of prediction requests that each deployed model in an endpoint should handle2.This way, you can minimize the impact to the existing and future model users, and monitor the performance of the new model over time2.

The other options are not suitable for your scenario, because they either require creating a separate endpoint or a Cloud Run service, which would increase the complexity and maintenance of your deployment, or they do not allow you to use traffic splitting, which would create a sudden change in your prediction results.Reference:

Introduction to Vertex AI Model Registry | Google Cloud

Deploy a model to an endpoint | Vertex AI | Google Cloud

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asked 18/09/2024
Jonas Junker
42 questions
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