Interactive Pipeline and Composite Estimators for Your End-to-End ML Model - Open Data Science - Your News Source for AI, Machine Learning & more

A data science model development pipeline involves various components including data injection, data preprocessing, feature engineering, feature scaling, and modeling. A data scientist needs to write the learning and inference code for all the components. The code structure sometimes becomes messier and difficult to interpret for other team members, for machine learning projects with heterogeneous data. A pipeline is a very handy function that can sequentially ensemble all your model development components. Using a pipeline one can easily perform the learning and inference tasks in a comparatively cleaner code structure.