Operationalizing Machine Learning Models - A Systematic Literature Review

Ask Berstad Kolltveit, Jingyue Li
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引用次数: 10

Abstract

Deploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. Although many studies focus on adapting ML software engineering (SE) approaches and techniques, few studies have summarized the status and challenges of operationalizing ML models. Model operationalization encompasses all steps after model training and evaluation, including packaging the model in a format appropriate for deployment, publishing to a model registry or storage, integrating the model into a broader software system, serving, and monitoring. This study is the first systematic literature review investigating the techniques, tools, and infrastructures to operationalize ML models. After reviewing 24 primary studies, the results show that there are a number of tools for most use cases to operationalize ML models and cloud deployment in particular. The review also revealed several research opportunities, such as dynamic model-switching, continuous model-monitoring, and efficient edge ML deployments. CCS CONCEPTS • General and reference → Surveys and overviews; • Computing methodologies → Machine learning; • Software and its engineering → Software development techniques.
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操作机器学习模型-系统的文献综述
将机器学习(ML)模型部署到生产环境中,其严格程度和自动化程度与传统软件系统相同,这是一项艰巨的任务,需要额外的关注和基础设施来应对额外的挑战。尽管许多研究集中于采用机器学习软件工程(SE)方法和技术,但很少有研究总结了机器学习模型的运行现状和挑战。模型操作化包括模型训练和评估之后的所有步骤,包括将模型打包成适合部署的格式、发布到模型注册中心或存储、将模型集成到更广泛的软件系统中、服务和监视。本研究是第一个系统的文献综述,研究了操作机器学习模型的技术、工具和基础设施。在回顾了24项主要研究后,结果表明,对于大多数用例来说,有许多工具可以用于操作机器学习模型,特别是云部署。该综述还揭示了几个研究机会,如动态模型切换、连续模型监控和高效边缘机器学习部署。CCS概念•一般和参考→调查和概述;•计算方法→机器学习;•软件及其工程→软件开发技术。
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