Deploying the model to an Azure Kubernetes Service (AKS) cluster is the appropriate choice for handling high traffic and providing low latency. AKS is designed for scalable deployment of containerized applications, making it suitable for production environments that require high availability and performance. Azure Container Instances are suitable for development and testing with lower traffic demands. Azure Batch is intended for large-scale parallel and batch computing jobs, not for real-time web services. Deploying as an IoT Edge module is for running models on edge devices, not for hosting scalable web services.
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Microsoft Azure AI Fundamentals AI-900
Describe Fundamental Principles of Machine Learning on Azure
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