title: Deploying MLflow Model without Conda environment
body: <p>Currently working on deploying my MLflow Model in a Docker container. The Docker container is set up with all the necessary dependencies for the model so it seems redundant for MLflow to also then create/activate a conda environment for the model. Looking at the documentation (<a href="https://www.mlflow.org/docs/latest/cli.html#mlflow-models-serve" rel="nofollow noreferrer">https://www.mlflow.org/docs/latest/cli.html#mlflow-models-serve</a>) it says you can serve the model with the <code>--no-conda</code> flag and that MLflow will assume you are “running within a Conda environment with the necessary dependencies”. This solution is working for us when we run in any environment with necessary dependencies, not necessarily a Conda environment. Is this correct? Or do we absolutely need to have a Conda environment active when running with the <code>--no-conda</code> flag?</p>
  
  <p>For example, I can create a virtualenv and, with the virtualenv active, serve the model locally using <code>mlflow models serve -m [model/path] --no-conda</code>. The model then performs properly, but the documentation makes it sound like this shouldn’t work because it explicitly calls for a Conda environment.</p>