Question

You have trained a scikit-learn model in an Azure Machine Learning notebook and logged it using mlflow.sklearn.log_model(). You want to deploy this model to a managed online endpoint without writing a custom scoring script or defining a custom environment. What must be true about the registered model for this no-code deployment to succeed?

Accepted Answer

The model must be registered as an MLflow model so Azure ML can auto-generate the scoring script and environment from the MLmodel metadata and signature.

Answer

The model must be registered as a custom model with a conda.yaml file attached manually to the model artifact.

Answer

The model must include an inline scoring script named score.py in the artifact root even for MLflow models.

Answer

The model must be converted to ONNX format before it can be deployed without a scoring script.