MLI is an Application Programming Interface (API) designed to address the challenges of building machine learning algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. A new research paper is available on the arXiv pre-print server which describes the new API.
MLI was developed by E. R. Sparks, et al. from the University of California, Berkeley and Brown University.
Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.