Once the team identied which is the target variable and the desired outcome of the model (e.g., if the intent of the model is diagnostics analytics or predictive ana­lytics), a model was recommended by the DS team. Because the manufacturing process could benet from both (target variable and desired outcome) to identify factors for growth/titer improvement and to predict the titer of culture for down­stream processing, a random forest model with explainable articial intelligence (AI) was used in this study. The random forest model was selected based on its strong prediction accuracy. It is good at capturing complex non-linear relationships in the data while being fairly robust to noise. To better interpret the complex and predictive models, an explainable AI (e.g., Shapley additive explanation [SHAP]) was used to generate insights [19]. When running the model, a vefold cross­validation was used. This is to reduce overtting of the available dataset and make the model outcome more robust to the broader dataset. The overall modeling pro­cess runs on Google cloud platform. The results were summarized in a global fea­ture importance plot for each model iteration, with the top 20 features, and their impact on scale and direction based on the SHAP value. To further understand the feature effect and model performance, the training and test sets R2, partial depen­dence plot, scatter plot, and trend stability were also reviewed.