A machine learning (ML) model needs to be deployed as a high-throughput, low- latency prediction service accessible via a REST API. The underlying…

Vertex AI Endpoints provide a fully managed service specifically designed for deploying machine learning models for online prediction. It meets all the stated requirements: it offers high-throughput and low-latency serving, automatically exposes the model via a REST API endpoint, and handles infrastructure management, including dynamic autoscaling based on prediction traffic. This makes it the ideal and recommended solution for hosting a production-ready ML prediction service on Google Cloud without managing the underlying compute resources.

A. Cloud Storage is an object storage service for data and model artifacts. It does not have the compute capabilities to load a model and serve predictions.

B. Running predictions locally in a browser (client-side) does not create a centralized, high-throughput backend service and relies entirely on the end-user's hardware.

D. A Managed Instance Group (MIG) is an Infrastructure as a Service (IaaS) solution. While it can scale, it is not "fully managed" as you are responsible for configuring the virtual machines, the runtime environment, and the prediction application code.

1. Google Cloud Documentation, Vertex AI, "Get online predictions from trained models": "After you deploy a model to an endpoint in Vertex AI, it's available to serve online prediction requests... Vertex AI prediction is a fully managed service that can scale up and down to meet the demand for your model's predictions." This source directly supports the choice of Vertex AI Endpoints for a fully managed, scalable, and API-accessible prediction service.

2. Google Cloud Documentation, Vertex AI, "Deploy a model to an endpoint": "To get online predictions, you must deploy your Model resource to an Endpoint. An endpoint is a resource that you can use to deploy one or more models... When you deploy a model to an endpoint, Vertex AI associates physical machine resources with the model so it can serve online predictions with low latency." This confirms that endpoints are the designated resource for low-latency online serving.

3. Google Cloud Documentation, Compute Engine, "Managed instance groups (MIGs)": "A managed instance group (MIG) is a group of virtual machine (VM) instances that you can manage as a single entity." This highlights that MIGs are a collection of VMs, which require user management of the OS and application stack, contrasting with the "fully managed" requirement met by Vertex AI.

4. Google Cloud Documentation, Cloud Storage, "Key terms": "Cloud Storage is a service for storing your objects in Google Cloud. An object is an immutable piece of data consisting of a file of any format." This reference defines Cloud Storage as a storage service, confirming it is unsuitable for serving predictions.