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MLOps vs LLMOps: the life cycle

Learn what happens after launch: MLOps (for trained ML models) — track experiments, register model versions, deploy, monitor data and prediction drift, retrain. LLMOps (for LLM apps) — version prompts and configs, trace every request, evaluate quality with test sets and user feedback, control cost and latency, guardrails, switch models safely. Both share: CI/CD, monitoring, alerts and rollbacks. Modules 2–5 build all of this step by step.