Fine-grained segmentation of content items for tiered cloud archival using machine-learned models

Abstract

Responsive to a request to store a content item for a user of a content service, user metadata and content item metadata are processed with a machine-learned embedding model to obtain a query embedding. A vector similarity search is performed on a vector database with the query embedding to retrieve a stored embedding matching the query embedding, wherein the stored embedding represents a contextual data element related to playback of the content item. A set of inputs is processed with a machine-learned Large Foundation Model to obtain an item storage decision output that identifies a segmentation point at which to segment the content item into first and second segments, wherein the item storage decision output further identifies first and second storage repositories for the first and second segments. The first segment is stored to the first storage repository and the second segment is stored to the second storage repository.