Question 1 of 30
A data scientist is tasked with deploying a machine learning model that predicts customer churn for a subscription-based service. The model needs to be accessible via a web service to allow real-time predictions. The data scientist considers using Azure Machine Learning for this purpose. Which of the following approaches would best facilitate the deployment of the model while ensuring scalability and ease of integration with other applications?
Deploy the model as a web service using Azure Kubernetes Service (AKS) to manage containerized applications.
Use Azure Functions to create a serverless function that invokes the model for predictions.
Deploy the model directly on a virtual machine (VM) without any orchestration tools.
Utilize Azure Logic Apps to automate the deployment process of the model.

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