在制造业中实现机器学习操作的行业成熟度模型

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Miguel Angel Mateo Casalí, Francisco Fraile Gil, A. Boza, A. Nazarenko
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引用次数: 0

摘要

该行业的下一个进化技术步骤假设工厂内的元件实现自动化,这可以通过广泛引入自动化、计算机和物联网(IoT)组件来实现。所有这些都寻求以尽可能低的成本精简、改进和增加生产,并遵循一项名为“零缺陷制造”的战略,避免产品生产中的任何失败。机器学习操作(MLOps)为这一挑战提供了一个基于ML的解决方案,促进了从开发到部署的所有产品相关步骤的自动化。在制造操作中集成不同的机器学习模型时,有必要了解需要什么功能和期望什么功能。本文提出了一个成熟度模型,可以帮助公司识别和映射其当前的机器学习模型实现水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An industry maturity model for implementing Machine Learning operations in manufacturing
The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the product, following a strategy called “Zero Defect Manufacturing”. Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing operations, it is necessary to understand what functionality is needed and what is expected. This article presents a maturity model that can help companies identify and map their current level of implementation of machine learning models.
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来源期刊
CiteScore
2.10
自引率
13.30%
发文量
18
审稿时长
20 weeks
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