GPgym让非机器学习专家也能轻松用高斯过程模型。
GPgym: A Remote Service Platform with Gaussian Process Regression for Online Learning
- 通过远程服务提供高斯过程回归，无需编写代码
- 支持跨领域专家在不改流程下接入机器学习
- 适合工程、科研等需快速集成模型的场景
机器学习已广泛应用于工业、工程和研究等领域。尽管众多成熟的机器学习模型已在GitHub等平台开源，但其部署通常需要使用Python、C++或MATLAB等特定编程语言编写脚本，这对非机器学习领域的专业人士构成障碍，难以将其算法融入现有工作流。为解决这一问题，我们提出基于高斯过程回归的远程服务节点GPgym，使来自不同领域的专家能够无缝且灵活地将机器学习技术集成到其现有的专用软件中，而无需编写或管理复杂的脚本代码。
原文摘要 · Abstract (English)
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.
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