Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.

Abstract: A non-transitory computer readable storage medium has instructions executed by a processor to maintain a repository of machine learning directed acyclic graphs. Each machine learning directed acyclic graph has machine learning artifacts as nodes and machine learning executors as edges joining machine learning artifacts. Each machine learning artifact has typed data that has associated conflict rules maintained by the repository. Each machine learning executor specifies executable code that executes a machine learning artifact as an input and produces a new machine learning artifact as an output. A request about an object in the repository is received. A response with information about the object is supplied.