I've been working with machine learning in production for more than a decade. The biggest challenges I see are actually organizational rather than algorithmic w

I've been working with machine learning in production for more than a decade. The biggest challenges I see are actually organizational rather than algorithmic when it comes to delivering value. Most of that comes down to the additional time and complexity ML adds to projects that are on an "agile" cadence. Models that do make it into production are at high risk of being permanently short circuited for simpler logic whenever a maintenance issue comes up and the original contributor, ever in high demand, has moved on to a new project, team or company. I'm a huge believer in the value of ML, but it seems hard right now to realize the vision. This is one idea I had to make ML simpler. How can we make things better?