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.

MLOps, maturity model, retail, machine learning, automation, AI governance, CI/CD for ML, Data Science, retail analytics, strategic planning for AI

Jana, A. K. (2023). Framework for Automated Machine Learning Workflows: Building End-to-End MLOps Tools for Scalable Systems on AWS. J Artif Intell Mach Learn & Data Sci, 1(3), 1-5. https://doi.org/10.51219/JAIMLD/aryyama-kumar-jana/151

Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (mlops): Overview, definition, and architecture. IEEE access, 11, 31866-31879. https://doi.org/10.1109/ACCESS.2023.3262138

Lima, A., Monteiro, L., & Furtado, A. P. (2022). MLOps: Practices, Maturity Models, Roles, Tools, and Challenges-A Systematic Literature Review. ICEIS (1), 308-320.

John, M. M., Olsson, H. H., & Bosch, J. (2025). An empirical guide to MLOps adoption: Framework, maturity model and taxonomy. Information and Software Technology, 183, 107725. https://doi.org/10.1016/j.infsof.2025.107725

Mahadevkar, S. V., Khemani, B., Patil, S., Kotecha, K., Vora, D. R., Abraham, A., & Gabralla, L. A. (2022). A review on machine learning styles in computer vision—techniques and future directions. Ieee Access, 10, 107293-107329. https://doi.org/10.1109/ACCESS.2022.3209825

Lu, Q., Zhu, L., Xu, X., Whittle, J., Zowghi, D., & Jacquet, A. (2024). Responsible AI pattern catalogue: A collection of best practices for AI governance and engineering. ACM Computing Surveys, 56(7), 1-35. https://doi.org/10.1145/3626234

Faubel, L., & Schmid, K. (2024, September). MLOps: A Multiple Case Study in Industry 4.0. In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-8). IEEE. https://doi.org/10.1109/ETFA61755.2024.10711136.

Pahune, S., & Akhtar, Z. (2025). Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models. Information, 16(2), 87. https://doi.org/10.3390/info16020087

Koning, R., Hasan, S., & Chatterji, A. (2022). Experimentation and start-up performance: Evidence from A/B testing. Management Science, 68(9), 1-46. https://doi.org/10.1287/mnsc.2021.4209.

Stone, J., Patel, R., Ghiasi, F., Mittal, S., & Rahimi, S. (2025, May). Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers. In 2025 IEEE Conference on Artificial Intelligence (CAI) (pp. 643-650). IEEE. https://doi.org/10.1109/CAI64502.2025.00118