This research examines MLOps-driven real-time obesity and heart disease risk monitoring systems to improve healthcare prediction. Secondary data assesses the literature on machine learning operations (MLOps) frameworks and predictive modeling in obesity-related cardiovascular risks. Continuous deployment of predictive models allows real-time changes based on the newest patient data, improving their flexibility and accuracy. Integrating electronic health information and wearable devices improves models' capacity to give timely and individualized healthcare. However, data protection and infrastructure for continual model retraining remain issues. Policy implications imply data security and equal access to modern healthcare technology need regulatory frameworks. Transparent AI standards in therapeutic contexts will also build confidence and responsibility. This study shows that MLOps frameworks may enhance obesity and heart disease management, enabling better preventative care methods for various populations.
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