Constraints of model deployment and production environments


Numerous frameworks are aiming to be the king of AI: among them we find Pytorch, Tensorflow, CNTK and MXNet as full frameworks and Keras and fastai as high level structures. To ensure interoperability, it is very important to find a common ground between different implementations, and tools to convert models from one framework to another. It is also very important to provide tools for “agnostic deployment”, freeing the developers on choosing the best framework to deploy, regardless of the deployment environment.