MLOps vs. LLMOps
NewBoth mean 'running AI models reliably in production', but the daily work differs. MLOps grew up around models a company trains itself: pipelines to retrain them, a registry of versions, monitoring for drift as data changes. LLMOps is about applications built on large language models, often bought rather than trained: tracking prompts, cost per request, latency, hallucinations and evals. Strong MLOps skills transfer partly, not fully.
How to tell them apart on a resume
MLOps
MLflow, Kubeflow, SageMaker, Vertex AI, feature stores, model registry, retraining pipelines, drift monitoring — models trained in-house.
LLMOps
Langfuse, LangSmith, Helicone, LLM gateways, prompt versioning, token costs, tracing, guardrails, evals — products built on LLMs.
The question that settles it
“Were the models you ran in production ones your team trained, or LLMs you called — and what did you monitor day to day?”
Read the full definitions
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