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Sepsis, a dysregulated immune-mediated host response to infection, is lethal, prevalent, and costly. It’s early detection has the potential to drastically reduce morbidity/mortality. We have developed a real-time cloud-based application that predicts onset-time of sepsis based on live ICU data and provides clinicians with actionable visual alerts. Clinicians and nurses can examine these alerts and initiate appropriate interventions. The prediction engine (DeepAISE) is a Deep Learning-based algorithm trained to reliably predict sepsis 4-6 hours in advance of clinical recognition. A scalable, cloud-based, system continuously streams bedside data and uses the prediction engine to generate hourly scores and displays these to clinicians. Interoperability is achieved through the use of FHIR resources and APIs. This system is monitoring ~100 patients on a daily basis at the Emory Tele-ICU center, and has been shown to reliably predict onset of sepsis with an AUC of 0.9.