Optimizing Cloud-Based Data Management in Banking Using Machine Learning and DevOps Practices

  • Authors

    • Dr. Kwame Nkosi Department of Economics, University of Ghana, Accra, Ghana. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2021PI5J1L

    Published 04-05-2021

  • Cloud Computing, Data Management, Banking Technology, Machine Learning, Devops, Data Governance, Financial Analytics, Ci/Cd, Mlops, Real-Time Processing

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    K. Nkosi, “Optimizing Cloud-Based Data Management in Banking Using Machine Learning and DevOps Practices”, IJAIDT, vol. 4, no. 1, pp. 01–11, Apr. 2021, doi: 10.67228/30713315/IJAIDT-2021PI5J1L.

  • Abstract

    In the rapidly evolving landscape of digital banking, data is a core asset that demands agile, secure, and scalable management strategies. This paper explores how cloud-based data management in the banking sector can be significantly optimized through the integration of Machine Learning (ML) techniques and DevOps practices. We examine the traditional challenges associated with financial data, including compliance, security, and performance, and demonstrate how modern cloud architectures coupled with intelligent automation and continuous delivery pipelines can enhance operational efficiency and data-driven decision-making. The paper also proposes an optimization framework that blends ML-powered analytics with DevOps workflows to support real-time data processing, improve data governance, and foster innovation. Case studies and current industry applications are discussed to underline the practical value of this interdisciplinary approach.

  • References

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