Normally, one important topic in DevOps is how we take care of automated creation and delivery of software artefacts.
With the rise of data science there is a new type of artefact - monolitic binary blobs representing a trained neural net for example or other machine learning models. Such a blob can have a size many GB and its creation is not yet standardized AFAIK which brings organizations back to the pre-CI age. Nevertheless, they have their version and associated collections of training data (corpora) which tend to grow rapidly as well.
What are best practices to address this new challenge using DevOps methods - if even possible?