Hi @robbe , 

My model was logged successfully using feature engineering client. The issue appears while creating a serving endpoint for that model.
For the Sklearn model I used a simple Random Forest model and for the spark model I am logging a pipeline with multiple steps like imputer, indexer, assembler and model.
Also, I am not using 3rd part feature stores here. I am using online tables within Unity Catalog, this is relatively a new feature introduced by Databricks. I am using same store with sklearn model and it is working fine, the issue appears only with spark pipeline.

While logging my model I am not specifying databricks-feature-lookup as dependency, it is being added by default with my model even if I give my own requirements file while logging the model.

Thanks