Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 259 discussion

Exam Professional Machine Learning Engineer topic 1 question 259 discussion - ExamTopics

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Problem Description

The question describes a scenario involving an application using a chain of scikit-learn models to predict product pricing. The goal is to deploy this workflow, manage model versions, ensure scalability (including scaling down to zero), minimize resource usage, and reduce manual effort.

Possible Solutions

Four options are provided:

A: Expose each model as a Vertex AI endpoint and create a custom container endpoint for workflow orchestration.
B: Create a custom container endpoint loading individual model files and track versions in BigQuery.
C: Expose each model as a Vertex AI endpoint and use Cloud Run for workflow orchestration.
D: Load models into Cloud Run and use it for orchestration, tracking versions in BigQuery.

Correct Answer

The suggested answer is C, indicating that using Vertex AI Endpoints for individual models and Cloud Run for workflow orchestration is the best approach.

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company’s products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

A. Expose each individual model as an endpoint in Vertex AI Endpoints. Create a custom container endpoint to orchestrate the workflow.
B. Create a custom container endpoint for the workflow that loads each model’s individual files Track the versions of each individual model in BigQuery.
C. Expose each individual model as an endpoint in Vertex AI Endpoints. Use Cloud Run to orchestrate the workflow.
D. Load each model’s individual files into Cloud Run. Use Cloud Run to orchestrate the workflow. Track the versions of each individual model in BigQuery.

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