How is MLOps different from DevOps?

DevOps manages systems whose behaviour is fully defined by code; MLOps manages systems whose behaviour also depends on data. That adds artifacts DevOps never had to version — datasets, model weights, feature definitions — and a failure mode DevOps never had: a service that still runs perfectly while its answers quietly go wrong.

Do I need MLOps for a small project?

You need the habits, not the platform. Even a single model in production deserves versioned training data, a reproducible pipeline and a basic accuracy check on live traffic. Heavy tooling pays off once several models or teams are involved.

What is MLOps? — Getting Digital glossary

MLOps

Also: ML operations, machine learning operations, LLMOps

MLOps is the engineering discipline of getting machine-learning models out of notebooks and into production — versioning, deploying, monitoring and retraining them — so they keep working after the world stops looking like the training data.

A model that only lives in a notebook is a prototype

Training a decent machine-learning model is the part demos are made of. Everything after that is MLOps: packaging the model so it can serve real requests, tracking which data and code produced which version, watching its accuracy in production, and retraining before quiet degradation becomes a visible failure. It borrows the habits of DevOps — automation, version control, monitoring — and adapts them to systems whose behaviour comes from data rather than only from code.

Versioning — models, training data and code are tracked together, so any prediction can be traced to what produced it.
Deployment & serving — models ship behind APIs or into batch pipelines with the same rigour as any other software release.
Monitoring & drift detection — production data changes over time; MLOps watches for the moment the model's world no longer matches its training.
Retraining loops — new data flows back into scheduled or triggered retraining instead of ad-hoc rescue projects.

The same discipline applied to large language models is often called LLMOps — the tooling shifts toward prompt versioning, evaluation suites and cost control, but the underlying question is identical: how do you run a learned system in production without being surprised by it? Solid data-engineering is the foundation either way, because every model is downstream of its pipelines.

Key takeaways

→MLOps is what turns a trained model into a maintained product — deployment, monitoring and retraining, not just training.
→Models degrade silently as data drifts; the monitoring loop is the difference between catching it and shipping wrong answers.

Related concepts

Broader topicMachine Learning
MLOps is the operational sub-discipline of machine learning.
RelatedData Engineering
Every production model sits downstream of data pipelines; the two disciplines share most of their plumbing.

FAQ

How is MLOps different from DevOps?: DevOps manages systems whose behaviour is fully defined by code; MLOps manages systems whose behaviour also depends on data. That adds artifacts DevOps never had to version — datasets, model weights, feature definitions — and a failure mode DevOps never had: a service that still runs perfectly while its answers quietly go wrong.
Do I need MLOps for a small project?: You need the habits, not the platform. Even a single model in production deserves versioned training data, a reproducible pipeline and a basic accuracy check on live traffic. Heavy tooling pays off once several models or teams are involved.

MLOps

A model that only lives in a notebook is a prototype

Key takeaways

Related concepts

Courses that teach this

FAQ