To publish a GPU-enabled model as a real-time web service in Azure Machine Learning, you must choose a deployment target that supports both real-time inferencing and GPU hardware. Azure Kubernetes Service (AKS): This is the recommended compute target for high-scale production deployments. You can create an AKS cluster with nodes that are GPU-enabled virtual machines, making it a suitable choice for deploying demanding deep learning models. Azure Container Instances (ACI): This is a serverless container solution ideal for development, testing, and low-scale, CPU-based workloads. However, ACI also offers support for GPU resources in specific regions, making it a viable option for GPU-enabled web service deployment without the complexity of managing a Kubernetes cluster.

C. Local web service: This deployment target is used for debugging and testing on a local computer. It is not a managed Azure compute target for publishing a production-ready web service.
D. Azure Machine Learning compute clusters: This compute target is designed for training machine learning models or running batch inference jobs, not for deploying models as real-time, interactive web services.

1. Microsoft Documentation - Deploy models with Azure Machine Learning: This document provides a table comparing deployment targets. It explicitly states that both Azure Kubernetes Service (AKS) and Azure Container Instances (ACI) support GPU for real-time inferencing.

Reference: Microsoft Corporation. (2023). Deploy models with Azure Machine Learning. Microsoft Docs. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?tabs=python#understand-deployment-targets. See the "Target compute" section and feature comparison.

2. Microsoft Documentation - Deploy a model to Azure Kubernetes Service: This guide details the process of deploying to AKS and mentions its suitability for production.

Reference: Microsoft Corporation. (2023). Deploy a machine learning model to Azure Kubernetes Service. Microsoft Docs. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service?tabs=python. The "Prerequisites" section discusses creating an AKS cluster

which can include GPU nodes.

3. Microsoft Documentation - Deploy a model to Azure Container Instances (ACI): This document explains deployment to ACI and notes its use cases.

Reference: Microsoft Corporation. (2023). Deploy a machine learning model to Azure Container Instances. Microsoft Docs. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-to-azure-container-instances. The document describes how to specify CPU and memory

and links to ACI documentation on GPU usage.

4. Microsoft Documentation - What are compute targets in Azure Machine Learning?: This document clarifies the distinction between training compute and deployment compute targets. It categorizes "Azure Machine Learning compute cluster" as a training target and AKS/ACI as deployment targets.

Reference: Microsoft Corporation. (2023). What are compute targets in Azure Machine Learning?. Microsoft Docs. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target#compute-for-deployment-inferencing.