Hi, by deployment strategies I meant by canary, blue-green...etc..etc. I figured this should be done by clearml-serving and maybe seldon as well.

SubstantialElk6 when you say "Triton does not support deployment strategies" what exactly do you mean?
BTW: updated documentation already up here:
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving

Hi, i'm gonna hijack this thread a bit. My community uses ClearML and is looking at various model deployment strategies. We are looking at a seamless integration with Triton but noted they Triton does not support deployment strategies. ClearML-Serving seems to but the strategies are rather limited. Is there a roadmap to expand Clearml-serving?

Hi DeliciousBluewhale87
This is the latest clearml-serving (stable release at GTC at the end of the month)
https://github.com/allegroai/clearml-serving/tree/dev

Generally speaking, clearml-sering is a control plane, preprocessing, ML inference, with Nvidia Triton for DL inference (fully transparent).
It allows you to spin an entire fully dynamic & scalable serving on top of k8s cluster. Once you spin the base containers, you can configure them live with a CLI, this includes adding new endpoint model serving including preprocessing code.

what does a control plane do ? I cant understand this..
Like the serving engine, will get the user input, preprocess, infer it and send back the results..

Hi DeliciousBluewhale87 , yes I think it does. Although I think ClearML-Serving works as a control plane on top of your serving engine.