Interactive augmented reality based optimization of machine learning model execution on hybrid cloud

A computer system, comprising:

• a processor that, when executing instructions stored in an associated memory, is configured to:
• receive 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;
• identify a resource requirement of the ML problem and available resources of the user;
• 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;
• enable a user configuration of the user defined contextual parameters in an interactive augmented reality (AR) view;
• identify a set of clusters for executing computing tasks of the ML problem, wherein the set of clusters is identified out of the available resources;
• implement a ML evaluation process to determine an optimized load distribution model for execution of the computing tasks within the set of clusters, wherein the ML evaluation process comprises distributing the computing tasks across the set of clusters;
• and
• execute the computing tasks to optimize load distribution model.

A computer program product comprising a computer readable storage medium storing instructions that, when executed by a processor, cause the processor to perform:

• 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 an 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.