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.

中文翻译：

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工程人工智能系统：研究议程

人工智能 (AI) 和机器学习 (ML) 在行业中越来越广泛地被采用，但是，基于十几个案例研究，我们了解到在系统中部署行业实力、生产质量的 ML 模型被证明具有挑战性。公司面临与数据质量、设计方法和流程、模型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，我们提供了公司在采用 ML 时所经历的典型演变模式的概念化，以及我们所研究的公司所经历的关键问题的概述。