AI Glossary
MLOps
A set of practices used to deploy, monitor, maintain, and improve machine learning systems in production.
MLOps
Overview
Deploying an AI model is not the end of the process.
In many ways, it is the beginning.
Once a model enters production, organizations must monitor performance, manage updates, maintain infrastructure, and respond to issues.
This is where MLOps becomes important.
MLOps stands for Machine Learning Operations.
It refers to the practices, processes, and tools used to deploy, monitor, maintain, and improve machine learning systems over time.
A helpful way to think about MLOps is maintaining a vehicle.
Buying a car is only the first step.
Regular maintenance keeps it operating safely and efficiently.
Similarly, MLOps helps organizations keep AI systems reliable after deployment.
MLOps often incorporates concepts from software engineering, automation, monitoring, testing, and CI/CD practices.
As AI systems become more important to business operations, MLOps is becoming an essential discipline for managing AI at scale.
Why It Matters
MLOps helps organizations keep AI systems reliable, secure, and effective over time.
Real-World Example
A company may use MLOps practices to monitor model performance and automatically deploy updated versions when improvements are available.