Towards evolutionary machine learning comparison, competition, and collaboration with a multi-cloud platform
Pasquale Salza, Erik Hemberg, Filomena Ferrucci, Una-May O’Reilly · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
We present cCube, an open source architecture used to automatically create an application of one or more Evolutionary Machine Learning (EML) classification algorithms that can be deployed to the cloud with automatic data factorization, training, result filtering and fusion. cCube enables automated EML classification algorithms comparison, competition and multi-party collaboration. It can be used by an algorithm developer, a community working together or a black box user of EML classification. It requires minimal extra code to cloud-scale shared-memory implementations. It employs a microservices architecture and software containers into which user code is integrated allowing to access to the full benefits of cloud computing, e.g., on demand and elastic computing, while not committing (code or patronage) to a specific cloud provider such as Amazon Web Services or OpenStack. We demonstrate cCube, straddling our application across two different cloud providers and replicate the collaborative activity at zero cost.