You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training. You must deploy the model to a context that allows for real-time GPU-based…

The correct answer is B. Azure Kubernetes Service. You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference. Inference, or model scoring, is the phase where…

Train and deploy models

Question

You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training. You must deploy the model to a context that allows for real-time GPU-based inferencing. You need to configure compute resources for model inferencing. Which compute type should you use?

Options

AAzure Container Instance
BAzure Kubernetes Service
CField Programmable Gate Array
DMachine Learning Compute

How the community answered

(36 responses)

A
19% (7)
B
69% (25)
C
8% (3)
D
3% (1)

Explanation

You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference. Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

Topics

#Model deployment#GPU inferencing#Real-time inference#Azure Kubernetes Service

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