The Role of MLOps in Healthcare: Enhancing Predictive Analytics and Patient Outcomes

Abstract

This comprehensive article explores the transformative role of Machine Learning Operations (MLOps) in healthcare, focusing on its impact on predictive analytics and patient outcomes. The article examines how healthcare organizations leverage MLOps frameworks to enhance model deployment, maintain regulatory compliance, and improve clinical decision-making processes. The article investigates the evolution of machine learning in healthcare, analyzing core components of healthcare MLOps implementation, including data pipeline management, model development, and monitoring systems. The article also addresses critical challenges in healthcare MLOps adoption, particularly in data privacy, model interpretability, and regulatory compliance, while providing insights into best practices for successful implementation in clinical settings.