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.

翻译：自动化机器学习（AutoML）旨在以最少的人工干预构建机器学习模型。尽管通用AutoML领域已有大量研究致力于将人类排除在人工智能（AI）应用构建的循环之外，但鲜有文献关注AutoML如何在开放环境场景中有效运作——例如大模型训练与更新流程、工业供应链或工业元宇宙场景。在这些场景中，人们在搜索过程中常面临开环问题：需持续收集数据、更新数据与模型、满足开发与部署环境要求、支撑海量设备、修改评估指标等。仅依赖数据驱动方法解决开放环境问题，需要大量数据、计算资源及专业数据工程师的投入，这导致现有AutoML系统与平台效率低下且计算可行性差。人机交互是解决开放环境AI问题的实用可行途径。本文提出OmniForce——一种以人为中心的AutoML（HAML）系统，融合了机器辅助人类与人类辅助机器两项技术，旨在将AutoML系统应用于实践，并在开放环境场景中构建自适应AI。具体而言，我们从机器学习版本管理、管道驱动的开发部署协作、灵活搜索策略框架、广泛配置的众包应用算法（含大模型）四个维度阐述OmniForce。此外，OmniForce构建的（大）模型可在数分钟内自动转化为远程服务——该过程被称为模型即服务（MaaS）。在多个搜索空间及实际用例中的实验结果表明，OmniForce兼具高效性与有效性。