MLOps
Machine Learning Operations
A set of practices and tools for developing machine learning models and keeping them running reliably in production.
In Simple Terms
"MLOps" is a system for embedding a finished AI model into a real service, running it efficiently, and continuing to improve it. It's used in things like recommendation engines for online shopping and image recognition for self-driving cars. Even as trends and conditions in the real world shift, teams can use fresh data to test, retrain, and update the model — keeping its predictions accurate over time.
Behind the Name
The name "MLOps" comes from combining "Machine Learning" and "Operations." It reflects the idea that once you build an AI model, the job isn't over — you keep feeding it new data, retraining it, and keeping it running reliably instead of just launching it and walking away.
Take a Closer Look!
"MLOps" is a management approach for building AI-powered systems efficiently and keeping them running reliably.
It's a framework where the team that builds the AI and the team that runs the system work together, keeping the processes for managing and operating AI running smoothly.
Unlike typical software, AI models can degrade in performance once they're running in production.
That's because as trends and people's preferences shift over time, a model trained on past data can no longer make correct predictions.
So you need a system for feeding in new data, retraining the model, and testing and rolling out the updated version.
To make that happen, MLOps automates and streamlines the whole pipeline — from collecting data, to retraining the AI, to testing its performance, to deploying it into production.
It also includes monitoring to check whether the AI currently in use is still working correctly.
In short, the goal of MLOps is to keep spinning that cycle of "build, test, deploy, and watch" for AI as fast and smoothly as possible.