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.

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