Our health is the consequence of a combination of intricate and stabilized interactions between genetic elements, microbiome configuration, environmental impulses, and lifestyle habits. Knowledge of their contributions, as well as the complex network that connects them, is essential to analyze the underlying mechanisms and the onset of many diseases and can provide crucial information on their prevention, diagnosis, and remedy. We have seen advancements in data analytic methods to discover valuable patterns by analyzing great amounts of non-standard, heterogeneous, unstructured, and incomplete healthcare data. Not only does it help in decision making, but also forecasting. With the limited accuracy identified in machine learning models, we have implemented a process framework of Machine Learning Operations (MLOps) to develop a robust and collaborative platform for streamlining data and process integration and synergy through the automation of retaining, testing, and deployment.