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Global Multidisciplinary Journal

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Automated Compliance and Governance in Cloud-Based Machine Learning Pipelines: Integrating MLOps, Auditability, and Regulatory Automation

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Owen B. Ashbourne 1 ,
4 Department of Information Systems and Digital Innovation, University of Melbourne, Australia

Abstract

The rapid institutionalization of machine learning across critical infrastructures, healthcare systems, financial services, and smart city platforms has transformed algorithmic pipelines into high consequence socio technical systems. As these systems increasingly process sensitive personal data, make consequential predictions, and become embedded into regulatory domains, compliance and governance can no longer be treated as peripheral or post hoc concerns. Instead, they must be integrated directly into the architecture of machine learning operations. This article develops a comprehensive theoretical and methodological framework for compliance oriented MLOps by synthesizing software engineering, data governance, fairness, auditability, and regulatory automation literatures. A central conceptual anchor is provided by the notion of compliance as code, in which regulatory requirements are expressed in machine readable, executable, and continuously auditable form inside cloud based machine learning pipelines. Building on the empirical and architectural insights of HIPAA as Code implemented in AWS SageMaker pipelines (European Journal of Engineering and Technology Research, 2025), this study positions automated audit trails not merely as logging mechanisms but as epistemic infrastructures that render algorithmic decision making visible, traceable, and contestable. Through an extensive interpretive and design oriented methodology, the article integrates MLOps theory, production readiness frameworks, technical debt analysis, fairness engineering, and governance oriented data literacy into a single coherent research program. The results demonstrate how compliance automation transforms the economics, ethics, and operational stability of machine learning systems by reducing regulatory drift, mitigating hidden technical debt, and enabling real time accountability. The discussion further situates these findings within broader debates about algorithmic governance, smart city infrastructures, and the future of regulated artificial intelligence, arguing that compliance as code is not simply a technical innovation but a reconfiguration of power, responsibility, and institutional trust in digital societies.

Keywords

MLOps governance, compliance as code, automated audit trails, cloud machine learning

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How to Cite

Owen B. Ashbourne. (2026). Automated Compliance and Governance in Cloud-Based Machine Learning Pipelines: Integrating MLOps, Auditability, and Regulatory Automation. Global Multidisciplinary Journal, 5(02), 19-25. https://www.grpublishing.org/journals/index.php/gmj/article/view/313

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