Use the MLflow UI to manually upload the model file and register it in the Model Registry.

Use the MLflow Client API to log the model as an artifact in an MLflow Run, and then register the model using the 'mlflow.register_model' function.

Manually create a new entry in the Model Registry and associate it with the model file stored in the MLflow tracking server.

Use the MLflow Client API to log the model as a metric in an MLflow Run, and then register the model using the 'mlflow.register_model' function.