Yes indeed you are right. Although I can imagine the production side becomes much more complex for larger scale deployments. So even though one gets better in navigating the deployment side of ML, as the applications get more scale and complexity, so does deployment. Therefore, perhaps the 80% remains a good rough estimate? What do you think, as you have more experience with complex deployments at large scale?