MLOps (machine learning operations) is the set of practices for deploying, monitoring, and maintaining machine learning models in production reliably — the ML counterpart to DevOps.
How it works
MLOps covers the full model lifecycle: versioning data and models, automating training and deployment pipelines, testing for quality and bias, and continuously monitoring live performance. When a model degrades or the data shifts, MLOps processes trigger retraining and a controlled re-release.
Why it matters for enterprise AI
Most AI value is lost between a working prototype and a dependable production system. MLOps closes that gap — turning one-off models into governed, observable services that keep performing as the business and its data change, which is what separates AI pilots from AI that ships.
