Explanation:
The solution meets the goal because registering the MLflow model in the Azure Machine Learning workspace is a required step before deploying to a batch endpoint. As confirmed by the community discussion (100% consensus for 'Yes' and detailed reasoning in comments), the model must be registered to be referenced during deployment. Once registered, you can create a batch deployment configuration using the existing AmlCompute cluster and batch endpoint. While additional steps like creating a deployment configuration are needed for full deployment, registering the model is the foundational step that enables the deployment process, making 'Yes' the correct answer.
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You have an Azure Machine Learning workspace containing an AmlCompute cluster and a batch endpoint. You clone a repository with an MLflow model to your local computer.
You need to deploy the model to the batch endpoint.
Solution: Register the model in the workspace.
Does the solution meet the goal?
A
Yes
B
No