MLOps — machine learning operations — is the set of practices, tooling, and discipline for running AI and machine learning systems reliably in production. It is the AI counterpart to DevOps: where DevOps covers building, shipping, and operating software, MLOps extends that to the parts unique to models — data pipelines, training and retraining, deployment of model artifacts, monitoring for quality drift, and the feedback loops that keep a system accurate over time.
A mature MLOps setup covers the full lifecycle. Data and features are versioned and validated so you know exactly what a model was trained on. Training is reproducible and automated rather than run by hand. Models are versioned, tested against evaluation suites, and deployed through a controlled process with the ability to roll back. Once live, the system is monitored not just for uptime and latency but for the things that quietly break AI — input distributions shifting away from training data, output quality degrading, costs creeping up.
In production, MLOps is what separates a demo from a dependable capability. A model that works in a notebook can fail in countless ways once it faces real traffic: the data feeding it changes, an upstream service alters a field, usage patterns drift, a new model version regresses on a case nobody tested. MLOps puts the guardrails, observability, and automation in place so these are caught and handled rather than discovered by users. It is also where cost discipline lives — tracking spend per request, caching, and right-sizing models.
MLOps matters because the launch is the beginning, not the end. Most of an AI system's life and most of its risk are in operation, and without operational discipline even a well-built model decays into something unreliable and expensive. Treating MLOps as an afterthought is the most common reason promising AI projects stall after their first deployment; treating it as a first-class concern is what lets a system improve continuously and scale safely.