You Think Your MLOps Can Scale GenAI? Think Again.

As organisations move from traditional machine learning to large language model (LLM) applications and AI agents, the operational landscape changes dramatically. While MLOps provided a foundation for deploying and managing ML models, the emergence of LLMs introduces new challenges in scale, architecture, governance, and observability. This session explores the critical differences between MLOps and LLMOps, highlighting why GenAI applications require dedicated tooling, processes, and design patterns. We’ll walk through the key components of a robust LLMOps pipeline, from data preparation, prompt management, and fine-tuning, to retrieval-augmented generation (RAG), evaluation, monitoring, and cost optimisation. Real-world examples and architectural patterns will demonstrate how organisations are evolving their ML infrastructure to meet the demands of production-grade LLM systems. By the end of this session, you’ll understand what it takes to operationalise GenAI at scale and why extending your MLOps stack simply isn’t enough.