Establishing a Robust LLMOps Framework for Intelligent Automation: Strategies and Best Practices

This paper delves into the establishment of a robust LLMOps framework, crucial for intelligent automation in modern enterprises. LLMOps for intelligent automation platforms require composability and capability to use different architectures, embeddings, and Generative AI models seamlessly with domain focus. In this paper we discuss an architecture we designed for UST SmartOps, UST's Intelligent Automation platform, keeping automation at its core. Large Language Models (LLMs) have significantly advanced AI capabilities in natural language processing and understanding, but their deployment and management, integration and using them effectively for automation use cases pose unique challenges. LLMOps, similar to DevOps and MLOps, is dedicated to the operationalization of these models, addressing infrastructure, performance optimization, security, and continuous improvement. The paper outlines the strategies and best practices essential for LLMOps, emphasizing data management, model monitoring, and the integration of AI with automation to improve operational efficiency, reduce costs, and improve decision-making processes. Through systematic orches-tration of tools, processes, and infrastructure, the document aims to provide a comprehensive guide to effectively leverage LLMs within intelligent automation frameworks.