MLOps (Machine Learning Operations)

The core problem MLOps solves is simple: AI models don't stay accurate forever. They degrade as real-world data changes, they break when fed unexpected inputs, and they require constant monitoring to catch problems before they hurt your business. Without MLOps, you end up with models that were accurate in testing but fail silently in production—costing you revenue and customer trust.

Think of it like managing a fleet of delivery trucks. You don't just build the trucks and send them out; you need mechanics checking them regularly, a system for refueling, a process for replacing worn parts, and a way to track performance. MLOps is the operational framework that keeps your AI models in that same state of readiness.

In marketing specifically, MLOps shows up when you're running personalization engines, predictive lead scoring, or churn prediction models. A marketing team might deploy a model that predicts which customers will unsubscribe. Without MLOps, that model might work great for three months, then degrade as customer behavior shifts seasonally. With MLOps, you have automated monitoring that flags when prediction accuracy drops below acceptable thresholds, automated retraining pipelines that update the model with fresh data, and version control so you can roll back if something breaks.

The practical implication for buying AI tools: ask vendors about their MLOps capabilities. Can they monitor model performance in real time? Do they handle retraining automatically? Can they explain why predictions changed? Tools without solid MLOps become liabilities—they require constant manual babysitting or they silently degrade, giving you false confidence in bad decisions.