MLOps is the set of practices for deploying, monitoring and maintaining machine-learning models reliably in production — the ML equivalent of DevOps. It covers pipelines, versioning, testing, serving, monitoring and retraining.
MLOps turns a model from a notebook experiment into a dependable production system: automated training/deploy pipelines, drift and quality monitoring, rollback, and reproducibility. It's what keeps AI reliable at scale.
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Lazlo engineers enterprise AI systems end to end — grounded, governed, in production.
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