LLMOps in Practice: Operating AI Beyond the Demo

LLM applications are moving fast from demos to real production systems, but many teams are still operating them like experiments. Traditional DevOps pipelines are great at deploying code, and traditional MLOps pipelines are great at managing data and models, but LLM-powered applications introduce new production assets: prompts, embeddings, vector indexes, retrieval pipelines, model gateways, evaluation datasets, guardrails, and agent workflows.

In this session, we will explore the new MLOps/DevOps stack for LLMs: how to version prompts, automate evaluation in CI/CD, deploy RAG pipelines, manage model endpoints, observe token usage and latency, control cost, secure access to enterprise knowledge, and use GitOps principles to promote AI changes safely across environments.

The goal is to show how DevOps and MLOps practices can transform LLMs from impressive demos into reliable, governed, and scalable production platforms.