机器学习系统：面向数据视角的综述
Machine Learning Systems: A Survey from a Data-Oriented Perspective
AI总结：
该综述从面向数据视角出发，采用系统化半自动化方法梳理ML系统中DOA的隐性应用情况，填补相关知识缺口，并提出促进ML系统部署的实用建议。
摘要：
随着AI技术的兴起，工程师们正将机器学习（ML）模型作为现实系统的组成部分进行部署。现实环境给这类系统的部署带来了挑战，因为这些环境会产生大量异构数据，且用户要求的响应效率越来越高。在部署基于ML的系统时，这些需求将主流软件架构推向了极限。\n面向数据的架构（Data-oriented Architecture，DOA）是一种新兴架构风格，能让系统更好地集成ML模型。尽管关于已部署ML系统的论文并未提及DOA，但其作者做出的设计决策隐性遵循了DOA。这种隐性决策造成了知识缺口，限制了从业者实现基于ML的系统的能力。\n本文综述了从业者为何、如何以及在多大程度上采用DOA来实现和部署基于ML的系统。我们通过回答这些问题、明确展示这些系统背后的设计决策与实践来填补知识缺口。本综述采用软件工程领域知名的系统化半自动化论文评审方法。多数被评审研究部分采用了DOA，这种采用方式使系统能够满足大数据管理、低延迟处理、资源管理、安全与隐私等需求。基于这些发现，我们提出了实用建议，以推动基于ML的系统的部署。
英文摘要：
Engineers are deploying ML models as parts of real-world systems with the upsurge of AI technologies. Real-world environments challenge the deployment of such systems because these environments produce large amounts of heterogeneous data, and users require increasingly efficient responses. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging style that equips systems better for integrating ML models. Even though papers on deployed ML systems do not mention DOA, their authors made design decisions that implicitly follow DOA. Implicit decisions create a knowledge gap, limiting the practitioners' ability to implement ML-based systems. \hlb{This paper surveys why, how, and to what extent practitioners have adopted DOA to implement and deploy ML-based systems.} We overcome the knowledge gap by answering these questions and explicitly showing the design decisions and practices behind these systems. The survey follows a well-known systematic and semi-automated methodology for reviewing papers in software engineering. The majority of reviewed works partially adopt DOA. Such an adoption enables systems to address requirements such as Big Data management, low latency processing, resource management, security and privacy. Based on these findings, we formulate practical advice to facilitate the deployment of ML-based systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
