GlossaryAI / LLM
What is MLOps?
MLOps is the practice of deploying, monitoring, and continuously improving machine-learning models in production.
Definition
MLOps is the operational discipline of running ML in production — covering experiment tracking (MLflow, Weights & Biases), model registries, A/B testing of model versions, drift monitoring, automated retraining, feature stores, and observability. Training a model is 20% of the work of an ML project; MLOps is the other 80%. At Paisol, MLOps is a default part of every machine learning engagement — without it, models silently degrade.
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