Hi there 🙂

I have used the Databricks Model Serving Endpoints to serve a model which depends on some config files and a custom library. The library has been included by logging the model with the `code_path` argument in `mlflow.pyfunc.log_model` and it works perfectly fine. I wanted to do the same with the config files but I couldn't make out where exactly the MLflow model was copied to on the Model Serving Endpoint build containing the MLflow model.

After a bit of debugging I figured out that for local builds of the MLflow model using `mlflow models build-docker` the model files are copied to `/opt/ml/model/` where I imagined that Model Serving Endpoints also used that command under the hood. I was wrong in that assumption as the model files were saved in `/model/` on the Serving Endpoints build.

My question is then finally, how and/or where do I get insights into the build process of the Model Serving Endpoints builds? This placement of the files seems to be part of a custom Dockerfile that I can't seem to find any specification or documentation for. It would also be amazing if it was possible to have interactive access to the container that hosts the Serving Endpoint as that would have made debugging a whole lot easier.

Thanks in advance 🙂