Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because AKS clusters in Azure Machine Learning are primarily designed and used as inference compute targets for model deployment and serving, not for training. While there are some advanced scenarios where AKS can be used for training (as mentioned in some community comments about preview features or Arc-enabled Kubernetes), the standard practice and Microsoft documentation clearly distinguish AKS as an inference target. The community discussion shows strong consensus (100% of answers and multiple upvoted comments) that AKS is for inference, not training. For training deep neural networks with GPU requirements, appropriate compute targets would be Azure ML Compute, Azure ML Compute Instances, or GPU-enabled virtual machines, not the AKS inference cluster.
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An IT department creates Azure resource groups and resources, including an Azure Machine Learning workspace and an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster.
You have a Microsoft Surface Book computer with a GPU, Python 3.6, and Visual Studio Code installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Proposed Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace. Run the training script as an experiment on the aks-cluster compute target.
Does the solution meet the goal?
A
Yes
B
No