LLMOps for Streaming Data: Bridging NLP and Event Pipelines

Large language model (LLM)-powered AI has created a new paradigm for the operationalization and deployment of AI systems, resulting in a new field of study, LLMOps. This study provides empirical metrics of a simulated production scale deployment, with a P95 inference latency of 470ms, and a token throughput of 28,000 tokens per second from an LLM with a hallucination rate of 4.2 percent, demonstrating the viability of LLM in real piping. This study examines how the integration of LLMOps with real-time streaming data and complex event processing (CEP) pipelines can combine NLP and high-velocity event-driven architecture. In addition to helping expand the prospective value of LLM in event driven systems, this project will explore intelligent text understanding, real-time decision making based on the text understanding, and reflexive engagement to advance discussion on LLM through a review of MLOps into LLMOps. Some of the preliminary ideas, including AgentOps, observability, and real-time optimization, will be presented and/or discovered in the framework of data streams. Practical deployment opportunities in various verticals, such as finance, human health, and cyber security, and latency, scale, and possible observability attack vectors will also be considered by the review. The more adaptive, explainable and scalable solutions of current event based systems will be deployed in the future work through the use of LLMOps.