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Answer: Deploy your models on Vertex AI endpoints.
The question emphasizes a small company with limited cloud expertise seeking an immediately available, scalable, reliable, and cost-effective solution that requires no additional infrastructure management. Vertex AI endpoints (Option D) are fully managed, requiring no setup or maintenance of underlying infrastructure, aligning with the 'no additional resources' requirement. In contrast, Compute Engine VMs (A) require manual VM management, Cloud Run (B) involves container orchestration and scaling configuration, and GKE (C) demands cluster management—all of which add operational overhead. The community discussion supports D, noting it 'requires no additional resources' and is the 'easiest option' given limited expertise, while C is debated due to the complexity of managing a GKE cluster. Thus, D is optimal for rapid deployment with minimal management.
Author: LeetQuiz Editorial Team
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As a lead ML architect at a small company migrating from on-premises to Google Cloud with limited cloud infrastructure expertise, what is the most suitable Google Cloud service for serving models that is immediately available, scalable, reliable, and cost-effective, while requiring no additional infrastructure management?
A
Configure Compute Engine VMs to host your models.
B
Create a Cloud Run function to deploy your models as serverless functions.
C
Create a managed cluster on Google Kubernetes Engine (GKE), and deploy your models as containers.
D
Deploy your models on Vertex AI endpoints.
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