US 11,853,017 B2
Machine learning optimization framework
Teodora Buda, Dublin (IE); Patrick Joseph O'Sullivan, Dublin (IE); Hitham Ahmed Assem Aly Salama, Dublin (IE); and Lei Xu, Dublin (IE)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Nov. 16, 2017, as Appl. No. 15/814,565.
Prior Publication US 2019/0146424 A1, May 16, 2019
Int. Cl. G05B 13/02 (2006.01); G06N 20/00 (2019.01); G06N 5/02 (2023.01); G06N 3/08 (2023.01); G06N 7/02 (2006.01)
CPC G05B 13/021 (2013.01) [G06N 20/00 (2019.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01); G06N 7/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components;
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a computational resource component that collects computational resource data associated with a group of computing devices that performs a machine learning process associated with batch learning, wherein the computational resource data comprises:
respective resource data of computing devices of the group of computing devices,
target algorithm data that identifies, from a group of algorithms, respective types of respective algorithms that were employed to determine the respective resource data;
respective states of the computing devices; and
respective amounts of execution time that the respective algorithms having the respective types took to determine the respective resource data based on the respective states of the computing devices;
a batch interval component that determines, based on the computational resource data and one or more changes to the respective states of the computing devices, a first batch interval defining a time interval to collect input data for the machine learning process; and
a machine learning component that provides the first batch interval to the group of computing devices to facilitate execution of the machine learning process based on the batch interval; and
wherein the batch interval component determines a second batch interval defining an updated time interval to collect the input data for the machine learning process in response to a control signal received from an electronic device that monitors the group of computing devices.