GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning

TL;DR

GPgym is a remote service platform utilizing Gaussian process regression that allows professionals across various fields to easily integrate machine learning into their workflows without programming in specific languages.

Contribution

The paper introduces GPgym, a novel remote service platform that simplifies the deployment of Gaussian process regression models for non-ML experts.

Findings

Enables seamless integration of ML models into diverse workflows

Reduces barrier for non-programmers to use machine learning

Facilitates flexible and accessible ML deployment

Abstract

Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. This dependency on particular languages creates a barrier for professionals outside the field of machine learning, making it challenging to integrate these algorithms into their workflows. To address this limitation, we propose GPgym, a remote service node based on Gaussian process regression. GPgym enables experts from diverse fields to seamlessly and flexibly incorporate machine learning techniques into their existing specialized software, without needing to write or manage complex script code.