clearml-serving does not support Spacy models out of the box among many others and that Clearml-Serving only supports following;
Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
Support Deep Learning Models (Tensorflow, PyTorch, ONNX).
An easy way to extend support to different models would be a boon.

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? For example, in this case Spacy? Another point to note is the 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?
This discussion can also touch on the points of how ClearML-Serving will evolve from this month's release.
Gluon
H2O
Keras
Prophet
PyTorch
XGBoost
LightGBM
Statsmodels
Glmnet (R)
SpaCy
Fastai
SHAP
Prophet
Pmdarima
Diviner
scikit-learn
Diabetes example
Elastic Net example
Logistic Regression example
TensorFlow
TensorFlow 1.X
TensorFlow 2.X
RAPIDS
Random Forest Classifier