Agreed, this describes our reasoning pretty well. Additionally, as we mentioned towards the end of the post, the incremental benefit of a model not supported by PMML is unlikely to be more significant than the incremental improvements we see from investing in improving our features and ground truth. As such, our lowest common denominator isn't actually the model, but rather the features and data pipelines. The suggestion sytelus makes is a perfectly good way of doing it though, and we will likely change our approach when we find that our models do become the lowest common denominator, or have a higher relative ROI for the time we invest in improving them.