Are you on an internal ML platform team? We'd love to know if self-service (for data scientists), multi-tenant model -> container image builds are a pain point

Are you on an internal ML platform team? We'd love to know if self-service (for data scientists), multi-tenant model -> container image builds are a pain point for you. I was lucky enough to be involved in the creation of https://chassis.ml/ https://chassis.ml/ - which we're open sourcing today! Basically, it solves the problem of "how do you get your MLflow models into KFServing" - and in the process solved some interesting problems around doing rootless image builds in a multi-tenant K8s cluster. In other words Chassis is an API server which muxes between MLflow models, k8s jobs + kaniko, KFServing + Modzy APIs, with a Python SDK on top for data scientists to drive it. Demo: https://www.youtube.com/watch?v=d_8OIfQOa3I https://www.youtube.com/watch?v=d_8OIfQOa3I Test drive! https://chassis.ml/#test-drive https://chassis.ml/#test-drive More broadly, we're working on a standard for making model serving more portable, so you can "build once, run many" i.e. build container images that run in a variety of different platforms, to avoid lock-in: https://openmodel.ml/ https://openmodel.ml/