Patents by Inventor KEVAN REHM

KEVAN REHM has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Machine learning pipeline performance acceleration with optimized data access interfaces using in-memory and distributed store
    Patent number: 12613788
    Abstract: Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.
    Type: Grant
    Filed: April 18, 2023
    Date of Patent: April 28, 2026
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Sridhar Balachandriah, Satish Kumar Mopur, Lance Mackimmie Evans, Sherin Thyil George, Kevan Rehm
  • MACHINE LEARNING PIPELINE PERFORMANCE ACCELERATION WITH OPTIMIZED DATA ACCESS INTERFACES USING IN-MEMORY AND DISTRIBUTED STORE
    Publication number: 20240354218
    Abstract: Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.
    Type: Application
    Filed: April 18, 2023
    Publication date: October 24, 2024
    Inventors: SRIDHAR BALACHANDRIAH, SATISH KUMAR MOPUR, LANCE MACKIMME EVANS, SHERIN THYIL GEORGE, KEVAN REHM