What is the term for a sequential set of processing that automates the data science process by feeding the output of one process into the input of the next process?

The term that best describes a sequential set of processing that automates the data science process by feeding the output of one process into the input of the next process is "pipeline." In data science, a pipeline refers to a series of interconnected data processing steps that streamline the workflow, ensuring efficiency and consistency in handling data from its raw form through to the final analysis or model deployment.

In the context of data science, pipelines play a crucial role in automating tasks such as data cleaning, transformation, feature selection, model training, and evaluation. By structuring these processes into a pipeline, data scientists can easily manage and reproduce results, allowing for better collaboration and scalability in projects.

While "workflow" may seem similar, it more broadly encompasses all the activities in a work process, which might include multiple pipelines and non-automated tasks. A "model" refers specifically to a mathematical representation used for prediction or analysis based on data, and a "framework" usually indicates a broader structural system or set of guidelines for building applications or processes but doesn't specifically describe the sequential processing aspect characteristic of a pipeline.