How can an Azure machine learning model be deployed?

Deploying an Azure machine learning model involves making the model accessible for predictions and integrating it into applications or services. Using Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) is a widely recognized and effective method for this purpose.

AKS provides a managed Kubernetes environment, which enables scalable and efficient deployment of containerized applications, including machine learning models. This allows for handling large volumes of requests and ensures that the model is resilient, easier to manage, and can be integrated with CI/CD pipelines for continuous updates.

On the other hand, ACI allows for quick deployment of containers without the overhead of managing a full Kubernetes cluster, making it a suitable choice for simpler or temporary deployments.

In contrast, other options mentioned do not provide the necessary infrastructure or scalability features required for machine learning model deployment. Microsoft Excel lacks the capability to serve models in a robust manner, and local servers may not support the same scalability or integration features. Azure Logic Apps, while useful for automating workflows, is not designed specifically for deploying machine learning models. Therefore, utilizing AKS or ACI is the preferred and correct answer for deploying Azure machine learning models.