Sure, pickle the object to a local file. Log it to your current run with mlflow.log_artifact. That's it. MLflow lets you log just about anything you want. However if you're experimenting with different variations on a sklearn Pipeline model, you could simply create and try each variation, log each directly with mlflow.sklearn.log_model, into one single MLflow experiment.
