Definition
Machine Learning is a branch of AI where systems learn patterns from data to make predictions or decisions, improving their performance over time without being explicitly programd for each specific task.
Importance
Machine learning underpins most of the AI capabilities businesses actually use day to day, from fraud detection to demand forecasting to recommendation systems, well before generative AI entered the conversation, and it often remains the better tool for problems with a clear, measurable outcome to predict. Understanding when a problem calls for a machine learning model, rather than a generative AI application, matters for getting the right, most cost effective solution rather than reaching for whichever technology is currently most talked about.
Application
We apply machine learning where a business problem has a clear, measurable target to predict, churn risk, demand levels, fraud likelihood, and sufficient historical data to train against, rather than defaulting to generative AI for every use case. This includes selecting the right model type for the specific problem and data available, and building the ongoing monitoring needed to catch model performance degrading as real world data shifts away from what the model was originally trained on, since an unmonitored model quietly gets worse over time without anyone noticing until its predictions are visibly wrong.
