The rapid expansion of mobile phone usage in low-income and middle-income countries has created unprecedented opportunities for applying AI to improve individual and population health. In benshi.ai, a non-profit funded by the Bill and Melinda Gates Foundation, the goal is to transform health outcomes in resource-poor countries through advanced AI applications. We aim to do so by providing personalized predictions and recommendations to support diagnosis to medical care teams and frontline workers, as well as to nudge patients through personalized incentives towards an improvement in disease treatment management and general wellness. To this end, we have built an operational machine learning platform that provides personalized content and interventions real-time. Multiple engineering and machine learning decisions have been made to overcome different challenges and to build an experimentation engine and a centralized data and model management system for global health. Databricks served as a cornerstone upon which all our data/ML services were built. In particular, MLflow and dbx (an opensource tool from Databricks) have been crucial for the training, tracking and management of our end-to-end model pipelines. From the data science perspective, our challenges involved causal inference analysis, behavioral time series forecasting, micro-randomized trials, and contextual bandits-based experimentation at the individual level. This talk will focus on how we overcome the technical challenges to build a state-of-the-art machine learning platform that serves to improve global health outcomes.