In the context of machine learning and artificial intelligence, a Pipeline is a sequence of steps that are executed in order to process and transform data before applying a machine learning model. Each step in the Pipeline is a data transformation that is applied to the input data and passes the transformed data to the next step in the pipeline.

Pipelining is a common technique in machine learning because it allows data scientists to automate the data preparation process, reduce the risk of errors and increase the reproducibility of results. For example, a Pipeline could include steps to pre-process data, such as normalisation or coding of categorical variables, followed by feature selection and hyperparameter optimisation before applying a machine learning model.

In addition to helping automate the data preparation process, the Pipeline can also help speed up the development of machine learning models by allowing data scientists to experiment with different data transformations and models without having to write repetitive code for each iteration. Popular machine learning libraries such as Scikit-learn in Python provide implementations of Pipeline that make it easy for data scientists and analysts to use.