GLOSSARY > MLOPS
MLOps
A set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
MLOps bridges the gap between data science experimentation and robust enterprise engineering. It encompasses model versioning, continuous evaluation, data drift detection, and automated retraining pipelines. Without MLOps, AI models degrade rapidly in production as the underlying data distribution of the real world shifts over time.
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