MLOps, short for Machine Learning Operations, is the discipline of running machine learning models in production safely and repeatably. A model that works in a notebook is only the start. MLOps covers everything after that: deploying it, watching how it performs, retraining it on fresh data and rolling out new versions without breaking what depends on them.
It helps to compare it to running a restaurant kitchen rather than cooking one good meal. Anyone can plate one impressive dish, but serving the same quality every night, with new ingredients and changing staff, needs a system. MLOps is that system for model deployment and ongoing model monitoring.
In practice this looks like a pipeline. Code and data are versioned, a new model is trained automatically when fresh data arrives, results are tested against the previous version, and only a model that passes is promoted to production. The same pipeline can roll back to the old version in minutes if the new one underperforms. Without that, every retrain is a manual scramble that only one person remembers how to run.
The reason it matters is that models decay. The world changes, customer behaviour shifts and a model that was accurate last year can quietly lose its edge, a problem called model drift. Good MLOps catches that early and triggers a fresh round of model training before the business feels the impact. The same ideas, adapted for large language models, are what people now call LLMOps.
At TopDevs we set up MLOps so a client’s models stay accurate without someone manually checking them every week, and so any developer on the team can safely ship an update.
