A machine learning engineer has registered a sklearn model in the MLflow Model Registry using...

The mlflow.sklearn module provides an API for logging and loading scikit-learn models. To load a model as an sklearn object for batch deployment, the mlflow.sklearn.load_model function can be used. This function takes a model_uri argument that specifies the location of the model to load. The model_uri can be a local file path, a remote file path, or a Databricks or MLflow Model Registry URI. The function returns an sklearn model object that can be used for inference or further training. The function also preserves the model signature and the input example, if they were logged with the model. References:

mlflow.sklearn Documentation, p. 1-2

MLflow Models Documentation, Model Signature and Input Example, p. 6-7