https://www.datarobot.com/blog/what-a-machine-learning-pipeline-is-and-why-its-important/
What a Machine Learning Pipeline is and Why It’s Important
https://databricks.com/glossary/what-are-ml-pipelines
ML Pipelines
Typically when running machine learning algorithms, it involves a sequence of tasks including pre-processing, feature extraction, model fitting, and validation stages. For example, when classifying text documents might involve text segmentation and cleaning, extracting features, and training a classification model with cross-validation. Though there are many libraries we can use for each stage, connecting the dots is not as easy as it may look, especially with large-scale datasets. Most ML libraries are not designed for distributed computation or they do not provide native support for pipeline creation and tuning.
https://spark.apache.org/docs/latest/ml-pipeline.html
ML Pipelines
In this section, we introduce the concept of ML Pipelines. ML Pipelines provide a uniform set of high-level APIs built on top of DataFrames that help users create and tune practical machine learning pipelines.