Hi @Landan George​ ,

Thanks for you prompt response and your detailed answers.

One point that I am not following, is regarding the integration with MLflow. Do you have a concrete example of that, in which using MLflow with feature store is easier, than using delta lake table.

One point that I thought, it could be the benefit of the feature store compared to normal delta lake table is, that the transformation in the feature preparation phase could get stored (for instance transform fitted functions of one-hot-encodig and min-max-scaler during training), so that once we deploy the model in the production and get a new batch of unseen raw data, we would not need to load the pickled fitted functions and apply it to the new data, Is my assumption wrong?

thanks!