Sorry yeah -- not saying nobody's using MLflow. We see teams and organizations using MLflow. What we don't see though is individual researchers/engineers/data scientists pick it up and use it.

From the people we have talked to who use MLflow, we hear it gets the job done, but individual contributors don't love it.

We really believe that widespread adoption comes from making something individuals love and use every day. That's the reason Docker was so successful, for example.

The lack of flexibility really resonates. That's the reason we're trying to be small and not too opinionated. We're something we can drop into your in-house system as a component, kinda like lots of deployment systems are built around Docker.