Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users' trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.
翻译:处理自治系统中人工智能(AI)模式的公司面临若干问题,例如用户对不利或未知条件缺乏信任,软件工程和AI模型开发之间的差距,以及在不断变化的业务环境中运作,本工作文件的目的是通过界定一种协调这两种活动的方法,缩小可靠的AIAS的发展与运作之间的差距。我们综合了AI基于AS在工业环境中的主要挑战。我们思考了克服这些挑战所需的研究工作,并提出了将之付诸实践的新颖的、整体的DevOps方法。我们阐述了四个研究方向:(a) 通过监测基于AIAS的业务和查明危急情况下的自我适应需要,增强用户的信任;(b) 综合灵活进程,以发展和演变AI模型和AS;(c) 在基于AI的AS的分布环境中继续采用不同背景的AI模型;(d) AI基于AS的整体DOps生命周期。