Definition
MLOps extends DevOps for machine learning. It covers data pipelines, model registries, feature stores, evaluation, deployment, monitoring, retraining, and governance.
How it works
Teams version data and models, test performance before deployment, monitor live behavior, and create rollback or retraining paths when data or model behavior changes.
Why it matters at work
Many AI pilots fail when they move from demo to production. MLOps makes AI systems reliable enough for real business workflows.
Workplace example
A bank monitors a fraud model for drift when transaction patterns change, then retrains and redeploys only after validation and approval.
Frequently Asked Questions
Do non-engineers need MLOps literacy?
Yes. Product, risk, legal, and operations teams need enough MLOps literacy to understand reliability, monitoring, and accountability questions before AI is deployed.