US 12,118,406 B2
Interactive augmented reality based optimization of machine learning model execution on hybrid cloud
Manjit Singh Sodhi, Bangalore (IN); Prerna Agarwal, New Delhi (IN); Shridhar Gurunath Domanal, Bengaluru (IN); and Sri Harsha Varada, Vizianagaram (IN)
Assigned to Kyndryl, Inc., New York, NY (US)
Filed by KYNDRYL, INC., New York, NY (US)
Filed on Jul. 29, 2021, as Appl. No. 17/443,983.
Prior Publication US 2023/0032748 A1, Feb. 2, 2023
Int. Cl. G06F 9/50 (2006.01)
CPC G06F 9/5072 (2013.01) [G06F 2209/501 (2013.01); G06F 2209/503 (2013.01); G06F 2209/508 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-based method for a cloud service brokerage, the method comprising:
receiving a data set and user defined contextual parameters relating to a machine learning (ML) problem of a user to be performed on the data set;
identifying a resource requirement of the ML problem and available resources of the user;
enabling a user configuration of the user defined contextual parameters in an interactive augmented reality (AR) view;
identifying a set of clusters for executing computing tasks of the ML problem, wherein the set of clusters is identified out of the available resources;
defining data pre-processing and feature engineering definitions for the data set based on associated metadata of the data set;
in response to identifying available memory space within individual clusters of the set of clusters, partitioning the data set across the available memory space within the individual clusters of the set of clusters such that a single cluster stores a single partition;
implementing a ML evaluation process to determine an optimized load distribution model for executing the computing tasks within the set of clusters, wherein the ML evaluation process comprises distributing the computing tasks across the set of clusters; and
executing the computing tasks to optimize the load distribution model.