The Terraform provider idea is interesting, I'll think about it more carefully. Almost all of our deployment configuration under the hood is done with Kubernetes (which is focused on the declaration of deployment state). We modeled our configuration after Kubernetes for that reason, and we want to go beyond low-level infrastructure configuration by allowing users to configure prediction tracking, model retraining thresholds, and other more ML specific features using the same declarative paradigm and in the same configuration files.