BioOps : Enabling MLOps for scalable and flexible model‐centric process control in biopharmaceutical development

Abstract The deployment of advanced modeling and machine learning in biopharmaceutical process control has been limited by legacy fragmented automation infrastructure and a lack of model lifecycle governance. We present BioOps, a modular automation framework for biopharmaceutical advanced process control (APC). The framework incorporates established Machine Learning Operations (MLOps) principles, including version‐controlled model registries, containerized deployment, and continuous integration/continuous delivery (CI/CD) pipelines. BioOps explicitly decouples model development from model execution within the control architecture. This decoupling enables process scientists to deploy, test, and iteratively refine mathematical models and control strategies in a flexible manner. These workflows can be applied across heterogeneous bioreactor platforms and are executed entirely in‐house, without reliance on vendor‐specific solutions or custom system integration. In addition, BioOps embeds traceability, auditability, and model lifecycle management directly into the control stack. As a result, the framework aligns with regulatory expectations while continuing to support agile, in‐house experimentation and development. Case studies across a geographically distributed set of bioreactors demonstrate BioOps' versatility in PAT‐driven feedback control, hybrid model‐based nutrient and glucose feeding, adaptive phase transitions, and cross‐scale deployment. By operationalizing model‐centric control through an MLOps‐inspired architecture, BioOps provides a practical foundation for scalable, reproducible, and future‐ready biomanufacturing.