Modern machine learning methods deliver strong predictive performance for bioprocess datasets, supporting productivity improvement and deeper process understanding. In this session, Seongjin Kim of Samsung Biologics demonstrates how XGBoost is used for prediction and how Bayesian optimization efficiently tunes model settings for robust performance on a bioprocess dataset. He will also show the use of Shapley values—an explainable AI (XAI) approach—to identify the process variables most strongly associated with productivity outcomes. Attendees will learn how these modern, data-driven methods can be applied to drive practical productivity improvements.