Describes the structured sequence of stages in the machine learning (ML) process, facilitating the flow of data from its raw state to a form that yields useful information. This setup ensures that each component of the ML process, from data preprocessing and feature extraction to model training and evaluation, is interconnected, with the output of one stage serving as the input for the next. Pipelines streamline the transformation of data, automate repetitive tasks, and enhance the reproducibility of ML workflows, thereby optimizing the efficiency and effectiveness of developing and deploying ML models. This concept is fundamental in managing complex data processing tasks, ensuring a smooth transition of data through various processing steps.