MLOps Explained: Running Machine Learning in Production

ProductionReliabilityPublished By Simon Budziak

MLOps is the engineering discipline for running machine learning models in production. It versions data and models, automates training and deployment, and monitors for drift, applying the habits of DevOps to systems whose behavior comes from data, so a model stays correct after it ships.

What problem does MLOps solve?

A trained model is correct on the day it ships and decays from there, because the world it predicts keeps moving while its training data stands still. MLOps exists to catch that decay and act on it: monitoring detects model drift, pipelines retrain on fresh data, and model validation gates the new version before it replaces the old. Without the discipline, the failure mode is silent: the model keeps answering, the answers get gradually worse, and nobody owns noticing.

How does MLOps differ from DevOps?

DevOps ships code, and code behaves identically until someone edits it. MLOps ships code plus data plus a trained artifact, and any of the three can invalidate the system. That adds work DevOps never needed: versioning datasets and features alongside code, making training runs reproducible, and testing statistical behavior rather than just functions. The deployable unit is an experiment result, not a build, which is why experiment tracking and model registries sit at the center of every MLOps stack.

What does MLOps look like in practice?

A loop. Data flows in and gets validated, training runs on a schedule or a trigger, candidate models pass automated evaluation against the current champion, deployment is staged, and production telemetry feeds AI observability dashboards that watch accuracy, latency and input distributions. The pipeline, not the model, is the product: teams that can retrain and redeploy in a day recover from drift as routine, teams that cannot treat every degradation as an incident.

How does MLOps relate to LLMOps?

LLMOps is MLOps extended for language model systems. The classic discipline assumed you trained the model; with LLMs most teams rent one, so versioning shifts from weights to prompts, evaluation shifts from labeled test sets to graded outputs, and cost moves from a one-time training bill to a per-call meter. The parts that transfer unchanged are the habits: version what defines behavior, gate releases with evaluation, and watch production instead of trusting launch-day numbers.

This entry was drafted with AI assistance.

Frequently asked questions

What is the difference between MLOps and DevOps?

DevOps manages code, which behaves the same until someone changes it. MLOps manages models, whose behavior degrades as the world drifts away from their training data, so it adds data versioning, retraining pipelines and drift monitoring.

Is MLOps still relevant now that companies use LLMs?

Yes. Language model systems need the same discipline plus new pieces, which is what LLMOps names. Classic MLOps still runs every fraud, forecasting and recommendation model trained on company data.

What does an MLOps pipeline include?

Versioned data and features, reproducible training, automated tests before deployment, a registry of model versions, staged rollout, and production monitoring that triggers retraining when accuracy or data quality slips.

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