Mateo-Casalí, Miguel ÁngelGil, Francisco FraileBoza, AndrésNazarenko, Artem2024-03-022024-03-0220232340-5317PURE: 84393923PURE UUID: 2f77d383-5e41-463f-86fd-012af78c9c60Scopus: 85168622939WOS: 001044161400006http://hdl.handle.net/10362/164358Publisher Copyright: © 2023 The Author(s).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.8468119engCMMISA-95Machine LearningManufacturing Execution SystemManufacturing OperationsMLOpsZero-defect ManufacturingBusiness and International ManagementStrategy and ManagementManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringSDG 9 - Industry, Innovation, and Infrastructurejournal article10.4995/ijpme.2023.19138https://www.scopus.com/pages/publications/85168622939