Today’s common machine learning architecture is not elastic and efficient at scale. It’s inevitable that we will need to find ways to scale machine learning better. With more digitization and implementation increasing across different industries, the data size and the complexity of machine learning models will increase as the algorithms evolve. Memory scaling and resiliency would require compute scaling as well to offer a more efficient and flexible fit for scale out architectures.‌