An MLOps Maturity Model for Retail Organizations and Transition Criteria Between Levels

Abstract

The article proposes an original MLOps maturity model specifically oriented toward retail organizations. The relevance of the study stems from the fact that, despite active investment in machine learning, many retailers face difficulties with scaling, ensuring reliability, and assessing the return on investment of AI initiatives. The presented model serves as a roadmap for the phased and systematic development of MLOps practices. The scientific novelty lies in the domain adaptation of general MLOps principles to the retail context and, critically, in establishing clear and measurable criteria for transitions between five maturity levels. The paper analyzes existing universal maturity models. The five levels, from chaotic to optimized, are described through the lens of four key dimensions: technology and data, ML development, deployment and operations, governance and people. Particular emphasis is placed on the development of concrete checklists that make it possible to verify readiness to transition to the next level. The purpose of the study is to provide retail companies with a tool for self-assessment and strategic planning to build their MLOps capabilities. To achieve this goal, methods of analysis of existing models, synthesis, and domain adaptation are used. In conclusion, it is emphasized that a high level of MLOps maturity is primarily a strategic rather than a purely technical task. The material is addressed to CDOs, CIOs, heads of Data Science, and MLOps engineers in retail.