Hi @Retired_mod thanks for getting back to me.

The link you attached is for installing private pip packages in a notebook. As mentioned in my question I can install my private package (that I uploaded to dbfs) in a notebook without issue. The problem I am having is installing this same package with model serving.

Running the command you gave me in a notebook results in a FileNotFoundException, while the directory is found with dbutils, see the screenshot below.

I copy-pasted the file path from my databricks notebook to my python code and tried this a number of times with a number of different path combinations. I am always getting the same error 

ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory

Furthermore, even if I can debug the issue i.e. why can the model serving docker build environment not find the file on dbfs (which i suspect is permission related), I'm not super happy with this workflow,  having to update private python packages in dbfs and having to update the link in the pip_requirements argument of mlflow.pyfunc.log_model.

What would make this process much easier is if a secret could be picked up by the build environment and then could be injected into the `conda.yaml` file via an init script. For example

# conda.yaml
channels:
  - defaults
dependencies:
  - python=3.10
  - pip
  - pip:
      - mlflow>=2.5.0
      - boto3>=1.28.18
      - company-private>=0.1.10
      - --index-url "https://aws:%%CODE_ARTIFACT_TOKEN%%@company-0123456789.d.codeartifact.eu-central-1.amazonaws.com/pypi/company-python-packages/simple/"
name: mlflow-serving

Then a .sh init script could do the following

sed -i "s/%%CODE_ARTIFACT_TOKEN%%/${{ secrets.code-artifact-token }}/g" conda.yaml 

I realize model serving currently does not support init scripts, is this on the roadmap? Or can you suggest another workflow so I can use private python packages?