Comment by mateiz

Don't MLflow Projects exactly meet this use case? A project lives in a Git repo, which can include both code and data, and specifies its software environment (currently Conda but will eventually also support Docker): https://www.mlflow.org/docs/latest/projects.html. You can then run it wherever you want to run code: CI system, Kubernetes, cloud, etc. The reason MLflow doesn't force people to use Projects is because many users like to develop ML in notebooks, but we definitely expect engineering teams to use it with Projects.