Enterprise Generative AI Deployment Through Advanced LLMOps Governance and Hybrid Cloud Infrastructure
DOI:
https://doi.org/10.21590/Keywords:
Enterprise Generative AI, Large Language Models, LLMOps, Hybrid Cloud Infrastructure, AI Governance, Model Lifecycle Management, Retrieval-Augmented Generation, Responsible AI, Cloud-Native Architecture, Model MonitoringAbstract
Enterprise adoption of Generative Artificial Intelligence (GenAI) is transforming organizational operations through intelligent automation, knowledge discovery, decision support, and advanced business process optimization. However, deploying Large Language Models (LLMs) across enterprise environments introduces challenges involving scalability, security, governance, model reliability, infrastructure interoperability, and operational costs. This research proposes an integrated enterprise Generative AI deployment framework combining advanced Large Language Model Operations (LLMOps) governance with hybrid cloud infrastructure. The proposed framework integrates model lifecycle management, automated deployment pipelines, continuous model evaluation, retrieval-augmented generation, responsible AI policies, and centralized observability. Hybrid cloud architecture enables organizations to distribute computational workloads across private infrastructure and public cloud platforms according to security requirements, performance objectives, and resource availability. Advanced governance mechanisms provide model version control, access management, data privacy enforcement, prompt monitoring, hallucination assessment, and regulatory compliance validation. The methodology incorporates automated orchestration, containerized model deployment, scalable inference services, and continuous feedback mechanisms to improve operational reliability. Performance evaluation considers response latency, model accuracy, resource utilization, deployment efficiency, security compliance, and operational cost. The framework aims to establish a secure, scalable, transparent, and manageable enterprise AI ecosystem. The proposed approach provides organizations with a structured foundation for adopting Generative AI while maintaining governance accountability, infrastructure flexibility, and sustainable AI-driven digital transformation.
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