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 - monolithic binary blobs representing a trained neural net for example or other machine learning models. Such a blob can be many GB in size and its creation is not yet standardized AFAIK which brings organizations back to the pre-CI age. Nevertheless, such blobs 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?