I agree that a lot of the challenges around production ML are organizational, but I think in many companies, it has more to do with a lack of engineering resources than it does the separation of eng and data science (though that certainly happens).
Building and maintaining ML infrastructure from scratch is a big project. That's why you see FAANG companies hiring for ML infrastructure/platform engineers. Most startups don't have the extra cycles for that big of an undertaking, and so you see a lot of slapped-together, hacky solutions to putting models into production.
I'm biased in that I work on Cortex ( https://github.com/cortexlabs/cortex ), but I think that open source, modular tooling that removes the need to reinvent the wheel is going to have a big impact in terms of making production ML more accessible.
Building and maintaining ML infrastructure from scratch is a big project. That's why you see FAANG companies hiring for ML infrastructure/platform engineers. Most startups don't have the extra cycles for that big of an undertaking, and so you see a lot of slapped-together, hacky solutions to putting models into production.
I'm biased in that I work on Cortex ( https://github.com/cortexlabs/cortex ), but I think that open source, modular tooling that removes the need to reinvent the wheel is going to have a big impact in terms of making production ML more accessible.