A data science team completes multiple training runs within an experiment by using MLflow.

MLflow training runs produce model artifacts — the serialized model files, conda environment, and MLmodel specification — stored in the run ' s outputs folder. These artifacts are transient run outputs but are not yet a versioned, named model that can be deployed. To make the model a first-class, versioned, deployable artifact, you must explicitly register it. Locating artifacts from the run (action A) is necessary because you need the run ' s artifact URI, typically in the form runs:/run_id/model, to register from. Registering in the AML workspace (action B) creates an entry in the model registry with a name and auto-incremented version, making the model discoverable, governable, and deployable across environments. Tagging the experiment (option C) does not version the model. Exporting to local storage (option D) removes the model from Azure ML ' s managed infrastructure, losing lineage and governance.

Microsoft Learn Reference Topic: Register MLflow models in the Azure Machine Learning model registry