论文标题
工程AI系统：研究议程
Engineering AI Systems: A Research Agenda
论文作者
论文摘要
人工智能（AI）和机器学习（ML）在行业中越来越广泛地采用，但是，基于十几个案例研究，我们了解到，在系统中部署行业强度，生产质量的ML模型被证明是具有挑战性的。公司经历了与数据质量，设计方法和流程，模型的性能以及部署和合规性相关的挑战。我们了解到，需要一种新的结构化工程方法来构建和发展包含ML/DL组件的系统。在本文中，我们提供了公司在使用ML时所经历的典型演化模式的概念化，并概述了我们研究的公司所遇到的关键问题。该论文的主要贡献是对AI工程的研究议程，概述了ML解决方案围绕ML解决方案的关键工程挑战，并概述了整个研究社区需要解决的开放项目。
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.
