[sidlls]

Seconding this. I have run a data science and machine learning team for the last couple of years. By far the most challenging part of our work has been convincing our data management team that we aren't just another front end widget factory and our development/operations staff that we aren't choosing "non-standard" tech to deliver model results into production. The model maintenance is difficult, too, due to poor data management practices but it's less challenging than the other items for my team.

[adamb]

What have you found to work best when coordinating with your data management and development/operations staff?

[sidlls]

Every organization and team is different. Often I've found two approaches work best: going around the road blocks and managing everything end to end, then getting buy-in for data and ops to own it properly after the fact (playing up the political angle of owning more stuff after we do the heavy lifting), and the brute force method of just meeting after meeting to educate people about the differences in use cases and deployment for ML products.

[sandGorgon]

same question here. im keen to understand this. Especially around responsibilities, OKRs and KRAs