LLMOps vs MLOps: What’s Different, What’s the Same, and How to Run Both in Production

This article is for engineers, data scientists, and tech leads who already understand basic machine learning but are figuring out how to run large language models in production. The goal is to explain llmops vs mlops in plain English, focusing on what actually changes when you move from classic ML models to generative AI systems. We’ll cover definitions, a side-by-side comparison, monitoring, integration patterns, and a practical checklist you can start using this week. MLOps in 5 Lines MLOps, short for machine learning operations, is the practice of taking traditional machine learning models — think fraud detection, churn prediction, or demand forecasting — from notebooks to reliable production services. The discipline covers data pipelines, model training, experiment tracking, model registries, model deployment, offline and online evaluation, and drift monitoring. MLOps standardizes how data scientists and ML engineers version datasets, model weights, and code so teams can reproduce