A Framework for Managing Configuration and Version Control of AI Models in DevOps-Driven Healthcare Systems
Keywords:
AI Model Governance, Healthcare DevOps, Configuration Management, Version Control, MLOps, Continuous Deployment, Model Drift, ReproducibilityAbstract
AI models in healthcare systems demand precision, adaptability, and traceability, especially within DevOps-driven environments where rapid iteration and deployment are routine. However, most healthcare organizations lack robust mechanisms for configuration and version control of AI models, leading to reproducibility issues, compliance risks, and integration failures. This paper proposes a structured framework that unifies DevOps principles with AI model lifecycle governance, focusing on configuration management, version control, and traceable workflows. The proposed model integrates continuous integration/continuous delivery (CI/CD), model registries, and audit trails tailored for healthcare standards. We evaluate the framework's theoretical underpinnings using historical best practices and highlight its implications in minimizing model drift and maximizing traceability.
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Copyright (c) 2026 Afram Enim, Khanita Ruhia (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
