OmniForce：面向以人为本、大模型赋能与云边协同的自动机器学习系统
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
AI总结：
本文提出以人为本的 AutoML 系统 OmniForce，通过人机协同、版本管理、流水线开发部署、灵活搜索策略及大模型服务化，解决开放环境场景下现有 AutoML 效率低和计算难以承受的问题。
摘要：
自动机器学习（AutoML）旨在以最少的人工投入构建机器学习模型。尽管围绕 AutoML 的总体研究已经相当丰富，目标是在构建人工智能（AI）应用时将人移出循环，但很少有文献关注 AutoML 如何在开放环境场景中良好运行，例如训练和更新大模型、工业供应链或工业元宇宙等过程。在这些场景中，人们在搜索过程中经常面临开环问题：他们必须持续收集数据、更新数据和模型、满足开发与部署环境的要求、支持海量设备、修改评估指标等。仅依靠纯数据驱动方法解决开放环境问题需要大量数据、计算资源以及专职数据工程师的投入，这使得当前的 AutoML 系统和平台效率低下且在计算上难以承受。人机交互是解决开放环境 AI 问题的一种切实可行的方法。在本文中，我们介绍了 OmniForce，一个以人为本的 AutoML（HAML）系统，该系统同时产出人类辅助机器学习（human-assisted ML）和机器学习辅助人类（ML-assisted human）技术，从而将 AutoML 系统付诸实践，并在开放环境场景中构建自适应 AI。具体而言，我们从以下方面介绍 OmniForce：机器学习版本管理；流水线驱动的开发与部署协同；灵活的搜索策略框架；以及广泛提供和众包的应用算法，包括大模型。此外，由 OmniForce 构建的（大）模型可以在几分钟内自动转化为远程服务；这一过程被称为模型即服务（MaaS）。在多个搜索空间和真实用例中获得的实验结果表明了 OmniForce 的有效性和高效性。
英文摘要：
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce.
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