Continuous Integration Pipelines for Lifecycle Management of Large Language Models

Abstract

The rapid evolution of large language models (LLMs) has introduced new challenges in model development, deployment, monitoring, and governance. Traditional software-focused Continuous Integration (CI) pipelines are insufficient for managing the iterative and data-intensive lifecycle of LLMs, which require continuous data validation, model retraining, bias and safety auditing, reproducibility checks, and scalable deployment. This paper proposes a comprehensive CI pipeline architecture tailored to the unique requirements of LLM lifecycle management. The framework integrates automated data quality assessment, modular training workflows, version-controlled model artifacts, continuous evaluation against multi-dimensional metrics, and responsible AI checks including fairness, robustness, and alignment. We discuss implementation patterns using modern MLOps tooling, highlight operational challenges, and present best practices for ensuring reliability, traceability, and ethical compliance in LLM-centric systems. The proposed approach facilitates faster iteration cycles, safer model updates, and more efficient long-term governance of LLM deployments.