MLOps is everything that keeps an AI system reliable after it's built — deploying it safely, watching for when it starts drifting or making worse predictions, and keeping it documented so anyone can see exactly how it behaves and why.

An AI model isn't "done" the day it ships. The world it's making predictions about keeps changing, and a model trained on last year's patterns can quietly get worse without anyone noticing — unless something is actually watching it. That's what MLOps is: the ongoing maintenance, not just the initial build.

How This Helps a Business

• Defense & Government: Deploying AI models on-premises or air-gapped, with monitoring so a degraded model gets caught and fixed — not silently trusted in a mission-critical role.
• Financial Services: Model monitoring and documentation built so an examiner or auditor can see exactly how a model behaves and why, on demand — not scrambled together after a finding.
• Small Business: Making sure the AI tool you paid for still works correctly six months later, instead of quietly degrading while nobody's watching.

This is the least visible capability on this list and often the most important one — it's the difference between an AI system you can actually trust over time and one that was only ever tested on day one.