server

As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing. A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus). towhee has an example to do exactly what you want with CLIP[1]. For icing on the cake the example notebook includes deployment of the model with Nvidia Triton Server[2], which IMO is hands down the best way to actually deploy an ML model[3]. [0] - https://towhee.io/ [1] - https://github.com/towhee-io/examples/tree/main/image/text_i... [2] - https://github.com/triton-inference-server/server [3] - https://news.ycombinator.com/item?id=35199418#35200266

"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