Hello @Edna 

Thank you for contacting Databricks community support.

MLflow allows you to save models using different "flavors," which are essentially different ways of serializing and deserializing models. When you specify flavor=mlflow.sklearn, you're telling MLflow to save the model using the scikit-learn flavor.

However, when you register the model in the model registry, MLflow will automatically create a pyfunc version of the model in addition to the scikit-learn version. This is because pyfunc is a generic flavor that can be used to load and serve models in a variety of environments, regardless of the flavor used to save the model.

So even though you specified flavor=mlflow.sklearn, the model will still be shown as pyfunc in the model registry. This is expected behavior and allows the model to be easily deployed in a variety of environments.

If you want to deploy the model using the scikit-learn flavor specifically, you can do so by specifying the flavor when you load the model from the registry. For example:

import mlflow
import xgboost as xgb

Load the model using the scikit-learn flavor
model = mlflow.sklearn.load_model(f"models:/{MODEL_NAME}/1")

Use the model to make predictions
predictions = model.predict(X_test)

In this example, mlflow.sklearn.load_model() is used to load the model using the scikit-learn flavor, even though the model is registered as a pyfunc in the model registry.