This is a guest authored post by Yu Chen, Senior Software Engineer, Headspace. Headspace's core products are iOS, Android and web-based apps that focus on improving the health and happiness of its users through mindfulness, meditation, sleep, exercise and focus content. Machine learning (ML) models are core to our user experiences by offering recommendations that engage users with new relevant, personalized content that builds consistent habits in their lifelong journey. Data fed to ML models is often most valuable when it can be immediately leveraged to make decisions in the moment, but, traditionally, consumer data is ingested, transformed, persisted and sits dormant for lengthy periods of time before machine learning and data analytics teams leverage it. Finding a way to leverage user data to generate real-time insights and decisions means that consumer-facing products like the Headspace app can dramatically shorten the end-to-end user feedback loop: actions that users perform just moments prior can be incorporated into the product to generate more relevant, personalized and context-specific content recommendation for the user.