Answer-first summary for fast verification
Answer: No
The solution does not meet the goal because adding a compute resource alone is insufficient for deploying an MLflow model to a batch endpoint. The workspace already has an AmlCompute cluster, so additional compute is not needed. To deploy the model, key steps include: 1) Registering the MLflow model in the Azure ML workspace (e.g., using MLflow's tracking URI or Azure ML SDK), 2) Creating a deployment for the batch endpoint that specifies the registered model and the existing compute target (AmlCompute cluster), and 3) Configuring the deployment with necessary settings like scoring script or environment if the model requires custom inference logic. The community discussion confirms this with upvoted comments stating 'No' and noting that model registration and batch endpoint creation are required, not just adding compute.
Author: LeetQuiz Editorial Team
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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: Add a compute resource to the workspace.
Does the solution meet the goal?
A
Yes
B
No