"TensorFlow has the better deployment infrastructure" Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both wa

"TensorFlow has the better deployment infrastructure" Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially). For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway. Triton Server is very impressive and I'm always surprised to see how relatively niche it is. [0] - https://github.com/triton-inference-server/server https://github.com/triton-inference-server/server