Towards Sustainable and Ethical Optimization of Large‑Language Models: Integrating CI/CD‑Driven MLOps and Responsible Governance

Abstract

The rapid proliferation of large‑language models (LLMs) has brought unprecedented capabilities in natural language understanding and generation, but also raised considerable concerns regarding scalability, reliability, ethical compliance, and operational sustainability. This article presents an integrative framework that unites Continuous Integration/Continuous Deployment (CI/CD) practices, MLOps and AgentOps methodologies, and governance-driven ethical considerations to optimize LLM performance in cloud-based environments. Drawing on recent theoretical and empirical works — including insights from CI/CD pipelines for LLMs (Chandra, 2025), governance frameworks for AI ethics (Newe et al., 2021), privacy and emerging dilemmas in sensor/data technologies (Durakbasa et al., 2023), and cross-disciplinary lessons from sustainability, accessibility, and healthcare integration — we elaborate a comprehensive model that addresses lifecycle management, performance monitoring, bias mitigation, compliance, and stakeholder accountability. The methodology section outlines a conceptual meta‑protocol for implementing continuous delivery of updated LLMs alongside rigorous governance audits. Results are interpreted through a descriptive lens, mapping anticipated benefits — improved responsiveness, reduced drift, ethical robustness, and stakeholder trust — and potential challenges — resource intensiveness, governance overhead, and unintended consequences. In discussion, we critically examine limitations, trade‑offs, and propose avenues for future research including automated ethical audits, community-inclusive governance, and integration with domain‑specific regulatory regimes. This article seeks to advance the discourse on responsible AI deployment by offering a scalable, ethical, and sustainable blueprint for organizations deploying LLMs at scale.