A Unified MLOps and Data Architecture Blueprint for Cross-Enterprise Decisioning in Global Financial and Tourism Ecosystems
Keywords:
Tourism, MLOps, Cross-Enterprise, Finance, Data ArchitectureAbstract
This study has outlined the factors where the individual MLOps and data architecture could improve operational efficiency, model performance, and cross domain business decision making in the financial and tourism business. The paper compares traditional silo-based systems to the typical Feature Store, frequent model contracts and a Model-as-a-Service implementation plan. It is also reported to have quantitatively enjoyed high feature reuse gains, reduced manual work, reduced pipeline failures and reduced deployment cycles. The integrated system also increases the speed of inferences and predictability of the different business units. The implication of these findings is that there is a possibility of scaling the machine learning, and overall quality of the decisions made by using a common architecture in large organizations.
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Kreuzberger, D., Kühl, N., & Hirschl, S. (2022). Machine Learning Operations (MLOPs): overview, definition, and architecture. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2205.02302
Granlund, T., Kopponen, A., Stirbu, V., Myllyaho, L., & Mikkonen, T. (2021). MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases. 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN), 82–88. https://doi.org/10.1109/wain52551.2021.00019
Shankar, S., Garcia, R., Hellerstein, J. M., & Parameswaran, A. G. (2022). Operationalizing Machine Learning: An interview study. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2209.09125
Soldatos, J., Troiano, E., Kranas, P., & Mamelli, A. (2022). A Reference Architecture Model for Big Data Systems in the Finance Sector. In A Reference Architecture Model for Big Data Systems in the Finance Sector (pp. 3–28). https://doi.org/10.1007/978-3-030-94590-9_1
Beese, J., Haki, K., Schilling, R., Kraus, M., Aier, S., & Winter, R. (2022). Strategic alignment of enterprise architecture management – how portfolios of control mechanisms track a decade of enterprise transformation at Commerzbank. European Journal of Information Systems, 32(1), 92–105. https://doi.org/10.1080/0960085x.2022.2085200
Bode, J., Kühl, N., Kreuzberger, D., & Hirschl, S. (2023). Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2302.01713
Wider, A., Verma, S., & Akhtar, A. (2023). Decentralized data governance as part of a data mesh platform: Concepts and approaches. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2307.02357
Orr, L., Sanyal, A., Ling, X., Goel, K., & Leszczynski, M. (2021). Managing ML Pipelines: feature stores and the coming wave of embedding ecosystems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2108.05053
Nambiar, A., & Mundra, D. (2022). An overview of data warehouse and data lake in modern enterprise data management. Big Data and Cognitive Computing, 6(4), 132. https://doi.org/10.3390/bdcc6040132
Lima, A., Monteiro, L., & Furtado, A. (2022). MLOps: Practices, Maturity Models, Roles, Tools, and Challenges – A Systematic Literature Review. Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022). https://doi.org/10.5220/0010997300003179
Jiang, W., He, Z., Zhang, S., Preußer, T. B., Zeng, K., Feng, L., Zhang, J., Liu, T., Li, Y., Zhou, J., Zhang, C., & Alonso, G. (2020). MicRoREC: efficient recommendation inference by hardware and data structure solutions. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2010.05894
Tu, D., He, Y., Cui, W., Ge, S., Zhang, H., Han, S., Zhang, D., & Chaudhuri, S. (2023). Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines. KDD ’23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4991–5003. https://doi.org/10.1145/3580305.3599776
Rutschi, C., Berente, N., & Nwanganga, F. (2023). Data sensitivity and domain specificity in reuse of machine learning applications. Information Systems Frontiers, 26(2), 633–640. https://doi.org/10.1007/s10796-023-10388-4
Haq, I. U., Pardaev, J., Allayarov, P., & Munir, S. (2023). The impact of financial innovation and infrastructure on inbound tourism in Central Asia. Journal of Open Innovation Technology Market and Complexity, 10(1), 100175. https://doi.org/10.1016/j.joitmc.2023.100175
Kurshan, E., Shen, H., & Chen, J. (2020). Towards Self-Regulating AI: Challenges and Opportunities of AI model governance in Financial Services. RePEc: Research Papers in Economics. https://doi.org/10.48550/arxiv.2010.04827
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