Do MLEs actually reduce your workload in your job?

Maybe I’m wrong, but I feel like in the bigger companies I have worked for, the “client - provider” kind of setup for MLEs / MLOps people and Data Scientists is broken.

Not having an MLE in the pod for a new model means that invariably when something is off with the serving, I end up debugging it because they have no context on what’s happening and if it is something that challenges the current stack, the update to account for it will only come months down the road when eventually our roadmaps align. I don’t feel like they take a lot of weight off my shoulders.

The best relationship I ever had with MLEs was in a small company where I basically handed off the trained model to them for deployment and monitoring, and I would advise only on what features were used and where they come from (to prevent a distribution mismatch in their feature serving pipelines online).

Discuss