Hi Jax! Thanks for the feedback, we really appreciate it 😄

MLFlow is able to support a multitude of models from dev to deployment. Is ClearML and ClearML-Serving going to support as much as well?

Do you mean by this that you want to be able to seamlessly deploy models that were tracked using ClearML experiment manager with ClearML serving?

I believe in such scenarios, a custom engine would be required. I would like to know, how difficult is it to create a custom engine with clearml-serving?

Do you want clearml serving to accept a "custom engine" argument that uses code you tracked using the experiment manager to serve it, or do you think it's better to have good documentation on how to write a custom/spacy/shap whatever you need extention for clearml-serving itself and then just deploy the space model for example using your self-built spacy engine?