Data Science pipelines and monolithic model blobs
Personally I don't see any reason for which an Artefact Repository - the recommeneded DevOps tool of managing artefacts - wouldn't be applicable to trained neural nets or other artefacts. 

The artefact size might have some upper limit for a particular artefact repository, but in such case it would be a technical or policy limitation one, not a fundamental/principial one.

As for applying DevOps methodologies for the process producing these artefacts, I think most if not all of them can be applied equally well, as long as the artefacts:


are produced from some sort of specification which supports change versioning (equivalent to software source code)
are built via a repeatable and automatable process
are validated using some sort of repeatable and automatable verification (similar to QA), eventually using some supporting data (training data in this case, equivalent to DB snapshots, for example)


Side note: monolithic software code delivery is still a big deal and is perfectly maintainable with DevOps methodologies (with a bit of care), not everything can be split in microservices. Size doesn't matter enough to make DevOps not applicable.