Deployable DMI prediction using GPBoost, MLOps, and flask without the need for exact birth dates

Introduction Accurate prediction of dry matter intake (DMI) is essential for genetic selection, ration formulation, and performance evaluation in beef cattle. However, direct measurement of DMI remains costly and impractical for large-scale or grazing operations, limiting its adoption as a routine management and breeding tool. Methods We developed a reproducible and deployable machine learning framework integrating Gaussian Process Boosting (GPBoost), Machine Learning Flow (MLflow) model management, Flask-based web application for inference, and Apache Airflow-driven automation to predict individual daily DMI from animal intake and climate data. Four model variants were evaluated: a Full Model incorporating all animal and climate features, and three core models that differed only in age estimation methods—age in days from date of birth (DOB), age in months from month of birth (MOB), and age in months derived from season of birth (SOB). Results All models demonstrated strong and consistent performance across training, testing, and validation datasets (R 2 = 0.69–0.74; RMSE = 1.13–1.22 kg). Core models showed less than 3% error increase relative to the Full Model, indicating that precise birth dates are not required for accurate prediction. Feature importance analysis identified bodyweight, age, water intake behavior, and test day as primary drivers of prediction accuracy, with climate variables contributing secondarily. Discussion This work presents a scalable, production-ready MLOps pipeline capable of delivering real-time livestock decision support for DMI prediction without requiring precise historical birth date information. The framework addresses practical constraints in large-scale and grazing operations while maintaining model accuracy, enabling broader implementation of data-driven management strategies in beef cattle production systems.