The term "machine learning pipeline" refers to a structured sequence of processes in AI development that outlines how data is prepared, modeled, and evaluated. This pipeline typically includes stages such as data collection, data preprocessing, feature selection, model training, and model evaluation. Organizing these steps in a pipeline ensures reproducibility, efficiency, and the ability to fine-tune models, making it easier to deploy machine learning solutions.