Machine Learning

Machine learning is how most practical AI systems work: instead of being written as explicit rules, the model learns by finding patterns across large amounts of example data. A fraud detection model learns what fraudulent transactions look like. A churn model learns which customer behaviors predict cancellation. The model doesn't know the rules in advance — it infers them from the data. That's also the core limitation: the model learns what was true in the past, which means performance degrades as the world changes, or fails outright when the training data was biased or unrepresentative.

Every production machine learning system has a ceiling set by the quality and relevance of its training data, and a shelf life set by how much the real world changes after training ends. A model trained on pre-pandemic customer behavior may give confidently wrong predictions today. A model trained on data from one customer segment may underperform for others. These aren't edge cases; they're the normal pattern of ML systems in production. Asking how the data was collected, what outcome the model was optimized for, and how often performance is reviewed is how leaders catch model failures before customers do.