MLOPS FOR LLMS: DEPLOYING AND MANAGING LARGE LANGUAGE MODELS AT SCALE

Abstract

Deploying and managing Large Language Models (LLMs) at scale introduces unique challenges beyond conventional MLOps, due to their massive parameter counts, dynamic behavior, and foundation model architecture. This paper proposes an integrated MLOps + LLMOps framework for development, CI/CD, deployment, monitoring, governance, and feedback loops. MLOps lifecycle models and critical success factors, then introduce recent advances incorporating LLM-specific best practices. Architecture visualization includes CI/CD pipelines, model registry, feature store, inference serving on Kubernetes or cloud-managed GPU services, drift monitoring, and prompt/version control. We present sequence diagrams of automated retraining, mind maps of component relationships, and evaluation charts. Empirical case studies illustrate orchestration and scaling considerations using Kubernetes, Spark, Vertex AI/SageMaker. Finally, this paper discusses open issues such as hallucination mitigation, model explainability, compliance, and socio technical integration.