While it's possible to use plain user and token for installing a custom dependency in an MLflow registered model, it's not recommended due to security reasons. Instead, Databricks suggests using Databricks secrets to securely store and reference secrets in notebooks or jobs. However, Databricks secrets are not directly accessible in the conda.yaml file, so a workaround such as creating a script to generate the conda.yaml file using the secrets might be necessary. Here's an example of a Python script that can be used to generate the conda.yaml file:

python
user = dbutils.secrets.get(scope="<my-scope>", key="<user>")
token = dbutils.secrets.get(scope="<scope>", key="<scope>")
company = "<company>"
git_package_project = "<git-package-project>"
version = "<version>"

conda_yaml_content = f"""
channels:
- conda-forge
dependencies:
- python=<version>
- pip==22.3.1
- pip:
- mlflow==2.8.1
- git+https://{user}:{token}@dev.azure.com/{company}/{git_package_project}@{version}
name: mlflow-env
"""

with open("conda.yaml", "w") as file:
    file.write(conda_yaml_content)

Replace the placeholders with your actual values.