This is a public list for the CDF MLOps SIG. All meetings and discussions are held in the open, and everyone is welcome to join. The current membership, calendar, and meeting documents can be found at

the Sig-MLOps 2021 roadmap speaks volumes to me and my experience as a machine learning phd student…

At this point in the development of the practice, it perhaps helps to understand that much of ML and AI research and development activity has been driven by Data Science rather than Computer Science teams. This specialisation has enabled great leaps in the ML field but at the same time means that a significant proportion of ML practitioners have never been exposed to the lessons of the past seventy years of managing software assets in commercial environments.

As we shall see, this can result in large conceptual gaps between what is involved in creating a viable proof of concept of a trained ML model on a Data Scientist’s laptop vs what it subsequently takes to be able to safely transition that asset into a commercial product in production environments. It is not therefore unfair to describe the current state of MLOps in 2020 as still on the early path towards maturity and to consider that much of the early challenge for adoption will be one of education and communication rather than purely technical refinements to tooling.

most other phd/professor type folks were not the least bit interested in dealing with these sorts of problems during development — basic codebase documentation often seems to be considered a waste of time based on the hundreds of github repos I’ve looked over.

it’s likely these are a subset of SEP fields and i’ve settled on calling the phenomena “Someone Else Will Clean Up the Mess” fields (or SEWCUM fields for short).