In the world of children's hospitals data exist in silos. Kids go undiagnosed with brain cancer or die on the operating room table because MRI scans or echocardiograms are difficult to share. While AI, in particular deep learning holds promise in diagnosing many conditions, there is little data to fuel training these algorithms. Silos of data were also a challenge in the world of computing until 1994, when the Internet connected 1,000,000 computing machines. As we know that changed our consumer lives. Our moon shot mission is to connect all 1,000,000 healthcare machines in all the children's hospitals in the world, and enable software applications and data to change children's lives. This talk will highlight four key priorities in developing AI in medicine. I’ll show our scalable high performance, global architecture to develop and deploy ML models based on distributed healthcare data which was engineered for security and privacy. Finally I’ll discuss how the architecture addresses the four key areas.