Training and deploying your PyTorch model in the cloud with Azure ML

This article explains the benefits of moving PyTorch models to the cloud using Azure ML.

It suggests that training models in the cloud can handle larger models and datasets than those trained on local machines.

The author provides instructions for training and deploying a Fashion MNIST model using Azure ML, outlining the steps to follow. They recommend training the model locally and ensuring that it works as expected before moving to the cloud.

The article guides readers through the process of creating and running an Azure ML command job to train and deploy their model.