Let me know if I understand you correctly, the main goal is to control the model serving, and deploy to your K8s cluster, is that correct ?
So good news (1) Dashboard is being worked on as we speak. (2) we released clearml-serving doing exactly that, the next release of clearml-serving will include integration with kfserving (under the hood) essentially managing the serving endpoints on top of the k8s cluster , wdyt?
I want to find a good deployment framework (if exists) and integrate it into our ClearML workflow).
like get latest release with prod tag from clearml api and apply that to k8s.
something along those lines
BTW: if you feel like pushing forward with integration I'll be more than happy to help PRing new capabilities, even before the "official" release
oh great!
let me take a look at this. There’s a lot of custom tooling so integration is very important.
cc: FiercePenguin76 ^^^
Hi DisgustedDove53
When you say "deployment" there are a lot of way to interpret that 🙂 what exactly are you looking for ?
Yes.
ClearML has all these awesome dashboards. I was thinking maybe there will be some deploy to prod/staging/shadow button so you can do everything from there. (Like Gitlab CI/CD if you have seen that)
That would be ideal.
Hi AgitatedDove14
For example version control, A/B testing, shadow testing, rollback etc..
We can use conventional solutions that we already use. Like Helm and Istio.
But for example is there any tools that gives more insight into model performance in production? or gives more knobs and settings specific for AI.
using common microservice deployment frameworks will be limiting.
I found some solutions (KubeFlow, Seldon Core, http://run.ai , MLFlow, …) but haven’t got to look at them really.