A pipeline is an ordered sequence of processing steps that moves data or artifacts through an AI or machine learning workflow. It may include ingestion, transformation, training, evaluation, deployment, and monitoring stages.
All of the operations needed to fit a model to a data set. A pipeline consists of data import, transformation, featurization, and learning steps. Once a pipeline is trained, it turns into a model.
The infrastructure surrounding a machine learning algorithm. A pipeline includes gathering the data, putting the data into training data files, training one or more models, and exporting the models to production. See ML pipelines in the Managing ML Projects course for more information.