Machine learning on data with no labels, where the model finds structure on its own, such as clusters of similar records.
Unsupervised Learning algorithms find clusters and structure in raw, unlabeled data. In the enterprise, they are commonly used for anomaly detection, flagging subtle deviations in network traffic or factory floor telemetry that engineers do not have time to review manually.
See also Enterprise Architecture, Large Language Model, Fine-Tuning and Model Inference.