Cross-platform flexible data model for dynamic storage, management, and retrieval of high-volume object data

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a cross-platform flexible data model for dynamic storage, management, and retrieval of high-volume object data, including multi-modal machine learning datasets. Indeed, in one or more implementations, the disclosed systems operate across a variety of different non-tabular media and multiple different digital repository platforms, to efficiently and flexibly store, manage, and retrieve machine learning datasets by utilizing a dynamic cross-platform metadata database and cross-platform file location database. In particular, the disclosed systems can dynamically evolve metadata for data files of various machine learning datasets and independently manage storage locations across digital repository platforms by maintaining and utilizing a centralized cross-platform metadata database and corresponding cross-platform file location database.