Learn the correct option for deploying a real-time inference pipeline as a service in Azure ML designer. Discover why AKS is the ideal choice for hosting your model.
Table of Contents
Question
You are using Azure ML designer to deploy a real-time inference pipeline as a service for others to consume. Where will you deploy the model? Select the correct option.
A. a local web service
B. AKS (Azure Kubernetes Service)
C. Azure ML compute
D. Azure containers
Answer
When using Azure ML designer to deploy a real-time inference pipeline as a service for others to consume, the correct option is B. AKS (Azure Kubernetes Service).
Explanation
Azure Kubernetes Service (AKS) is the ideal choice for deploying models as web services because it provides a scalable, managed platform for running containerized applications. When you deploy your trained model to AKS, it automatically scales based on demand to ensure high performance and availability for the inference pipeline service.
The other options are not suitable for this scenario:
A. A local web service would not be accessible to other users, as it runs only on your local machine.
C. Azure ML compute is used for training models, not deploying them as services.
D. While Azure containers could potentially be used, AKS provides a fully managed solution specifically designed for running containerized services.
Therefore, AKS is the correct choice for deploying your real-time inference pipeline as a service that can be consumed by others. Azure ML designer seamlessly integrates with AKS, making the deployment process straightforward and efficient.
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