Maturity Model for Analysis of Machine Learning Operations in Industry

Miguel Ángel Mateo-Casalí; Francisco Fraile; Andrés Boza; Artem Nazarenko

doi:10.1007/978-3-031-27915-7_57

Maturity Model for Analysis of Machine Learning Operations in Industry

Miguel Ángel Mateo-Casalí, Francisco Fraile, Andrés Boza, Artem Nazarenko

UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through 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 have a good understanding of 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.

Original language	English
Title of host publication	IoT and Data Science in Engineering Management
Subtitle of host publication	Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización
Editors	Fausto Pedro García Márquez, Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, Alba Muñoz del Río
Place of Publication	Cham
Publisher	Springer
Pages	321-328
Number of pages	8
ISBN (Electronic)	978-3-031-27915-7
ISBN (Print)	978-3-031-27914-0
DOIs	https://doi.org/10.1007/978-3-031-27915-7_57
Publication status	Published - 2023
Event	16th International Conference on Industrial Engineering and Industrial Management - Toledo, Spain Duration: 7 Jul 2022 → 8 Jul 2022 https://ingenium.uclm.es/index.php/cio-2022/

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer
Volume	160
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Conference

Conference	16th International Conference on Industrial Engineering and Industrial Management
Abbreviated title	CIO2022
Country/Territory	Spain
City	Toledo
Period	7/07/22 → 8/07/22
Internet address	https://ingenium.uclm.es/index.php/cio-2022/

Keywords

CMM
ISA-95
Machine learning
Manufacturing execution system
Manufacturing operations
MLops
Zero-defect manufacturing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-27915-7_57

Cite this

Mateo-Casalí, M. Á., Fraile, F., Boza, A., & Nazarenko, A. (2023). Maturity Model for Analysis of Machine Learning Operations in Industry. In F. P. G. Márquez, I. S. Ramírez, P. J. B. Sánchez, & A. M. del Río (Eds.), IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización (pp. 321-328). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 160). Springer. https://doi.org/10.1007/978-3-031-27915-7_57

Mateo-Casalí, Miguel Ángel ; Fraile, Francisco ; Boza, Andrés et al. / Maturity Model for Analysis of Machine Learning Operations in Industry. IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización. editor / Fausto Pedro García Márquez ; Isaac Segovia Ramírez ; Pedro José Bernalte Sánchez ; Alba Muñoz del Río. Cham : Springer, 2023. pp. 321-328 (Lecture Notes on Data Engineering and Communications Technologies).

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title = "Maturity Model for Analysis of Machine Learning Operations in Industry",

abstract = "The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through 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 have a good understanding of 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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author = "Mateo-Casal{\'i}, {Miguel {\'A}ngel} and Francisco Fraile and Andr{\'e}s Boza and Artem Nazarenko",

note = "Funding Information: Acknowledgements. The research leading to these results received funding from the European Union H2020 programs with grant agreements No. 825631 “Zero-Defect Manufacturing Platform (ZDMP)” and No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 16th International Conference on Industrial Engineering and Industrial Management, CIO2022 ; Conference date: 07-07-2022 Through 08-07-2022",

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Mateo-Casalí, MÁ, Fraile, F, Boza, A & Nazarenko, A 2023, Maturity Model for Analysis of Machine Learning Operations in Industry. in FPG Márquez, IS Ramírez, PJB Sánchez & AM del Río (eds), IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización. Lecture Notes on Data Engineering and Communications Technologies, vol. 160, Springer, Cham, pp. 321-328, 16th International Conference on Industrial Engineering and Industrial Management, Toledo, Spain, 7/07/22. https://doi.org/10.1007/978-3-031-27915-7_57

Maturity Model for Analysis of Machine Learning Operations in Industry. / Mateo-Casalí, Miguel Ángel; Fraile, Francisco; Boza, Andrés et al.
IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización. ed. / Fausto Pedro García Márquez; Isaac Segovia Ramírez; Pedro José Bernalte Sánchez; Alba Muñoz del Río. Cham: Springer, 2023. p. 321-328 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 160).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mateo-Casalí, Miguel Ángel

AU - Fraile, Francisco

AU - Boza, Andrés

AU - Nazarenko, Artem

N1 - Funding Information: Acknowledgements. The research leading to these results received funding from the European Union H2020 programs with grant agreements No. 825631 “Zero-Defect Manufacturing Platform (ZDMP)” and No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through 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 have a good understanding of 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.

AB - The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through 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 have a good understanding of 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.

KW - CMM

KW - ISA-95

KW - Machine learning

KW - Manufacturing execution system

KW - Manufacturing operations

KW - MLops

KW - Zero-defect manufacturing

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AN - SCOPUS:85151568565

SN - 978-3-031-27914-0

T3 - Lecture Notes on Data Engineering and Communications Technologies

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EP - 328

BT - IoT and Data Science in Engineering Management

A2 - Márquez, Fausto Pedro García

A2 - Ramírez, Isaac Segovia

A2 - Sánchez, Pedro José Bernalte

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PB - Springer

CY - Cham

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ER -

Mateo-Casalí MÁ, Fraile F, Boza A, Nazarenko A. Maturity Model for Analysis of Machine Learning Operations in Industry. In Márquez FPG, Ramírez IS, Sánchez PJB, del Río AM, editors, IoT and Data Science in Engineering Management: Proceedings of the 16th International Conference on Industrial Engineering and Industrial Management and XXVI Congreso de Ingeniería de Organización. Cham: Springer. 2023. p. 321-328. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-3-031-27915-7_57

Maturity Model for Analysis of Machine Learning Operations in Industry

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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