Hi friends, I'm just seeing these new messages. I read these links and I agree with @<1557175205510516736:profile|ShallowSwan53> . It's nice that the webapp has these pages, but what is the workflow to actually use this registry?

Also, @<1557175205510516736:profile|ShallowSwan53> , do you have a specific workflow in mind that you're hoping to get from ClearML?

At BEN, we're experimenting with

• BentoML for model serving. It's a Python REST framework a lot like FastAPI, but with some nice utilities for inference.
• MLFlow. We'd be open to replacing this with ClearML. I'd even push for it just for the nice integrations with the experiment tracking part of ClearML!
  Here's our MLFlow workflow

What if I wanted to do something like:

• run several experiments, logging a model artifact for each, landing in S3 as a backend
• compare the experiments and promote the best model, maybe with tags like production and click-through-rate-regressor and v1.0.0 . Advanced, stable pipelines may automate this step if the selection criteria are clear.
• Run a script from CI, possibly in Python, that- fetches the latest click-through-rate-regressor model with the production tag from the registry
• clones / installs the inference and preprocessing code that goes with that model--could be an entire REST API with that logic. Monitoring code with something like WhyLabs or Arize.ai would go in the REST API endpoints.
• builds a docker image containing the REST API and the model weights baked into the image
• deploys that image to wherever you run containers (we use AWS ECS)
  (1) and (2) seem doable to me, but (3.a) I'm not sure about.

Is there a straightforward way we could do a filter query against ClearML for model matching these criteria, and then download it?