Configurable Parallel Induction Machines

Machine Learning practice in general offers significant opportunities for parallel computing and practicing sound software engineering. More often than not, practitioners routinely write dataset specific scripts and learners focus on model building and refining. Focusing on particular models is not consistent with NFL, a fundamental theorem in Machine Learning. Not minding time-honored software engineering principles is inefficient. In this paper, we present our implementation of MISD machine, consistent with No Free Lunch Theorem, problems we encountered and our approach to solve those problems.