How can I deploy a PyTorch model using Azure ML SDK?
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How can I deploy a PyTorch model using Azure ML SDK?
To deploy a PyTorch model using Azure ML SDK, you need to follow these steps: 1. Register your model, 2. Create an inference configuration, 3. Deploy the model to an Azure container instance or Azure Kubernetes Service.
What are the prerequisites for deploying a model?
You need an Azure account, the Azure ML SDK installed, and your PyTorch model saved in a format compatible with Azure.
Can you explain how to create an inference configuration?
Sure! An inference configuration includes the environment details and the entry script. You can create it using the InferenceConfig class in Azure ML SDK.
What if I want to scale my deployment?
For scaling, you can deploy your model to Azure Kubernetes Service, which allows you to manage multiple instances and scale based on demand.
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