MLOps (Machine Learning Operations) is the set of practices for reliably building, deploying, monitoring, and maintaining machine learning models in production. It brings engineering discipline to the messy business of running AI in the real world.
What it means in plain English
Building a model is only the start; keeping it working reliably in a live product is a bigger challenge. MLOps covers the whole lifecycle: versioning data and models, automating training and deployment, monitoring performance, and updating models as the world changes. It borrows ideas from software engineering (DevOps) and adapts them to the extra complexities of data and models.
Without MLOps, models tend to degrade quietly and break in production.
A simple example
An MLOps setup automatically retrains a fraud-detection model on fresh data each week, tests it, deploys it if it passes, and alerts the team if its accuracy starts to drop — keeping the live system reliable without constant manual work.
Why it matters
MLOps is what turns a promising model into a dependable production system. As organisations rely on AI for real decisions, the discipline of deploying and maintaining models well becomes as important as building them in the first place.
Related terms
- Model Drift — a problem MLOps monitors for.
- Data Pipeline — a core part of MLOps.
- Inference — the production process MLOps supports.
Frequently asked questions
What is MLOps?
MLOps is the set of practices for deploying, monitoring, and maintaining machine-learning models in production reliably — the “DevOps” of machine learning.
Why is MLOps needed?
Getting a model to work in a notebook is only the start; MLOps handles versioning, deployment, monitoring for drift, and retraining so models stay reliable in the real world.
