GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
This addresses the problem of accessibility for professionals outside ML, enabling easier adoption in various domains, though it is incremental as it builds on existing Gaussian process methods.
The paper tackles the barrier of deploying machine learning models due to language dependencies by proposing GPgym, a remote service platform based on Gaussian process regression, which allows non-experts to integrate ML into their workflows without coding.
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.