We have achieved significant improvements in Machine Learning along with computing infrastructure. Many varieties of machine learning algorithms have been proposed, implemented and deployed. More often than not, these deployments are unitary learners with a clever refinement, the so called hyper-parameter tuning. While it is definitely a step in the right direction, the strategy is depth first and runs counter to both Ockhams Razor and No Free Lunch (NFL), two fundamental axioms in Machine Learning. Raman and his research students have engineered a system that provides an alignment with both the razor and NFL. In this talk we will introduce NFL and Razor axiomatically and demonstrate a system that remains consistent with NFL and facilitates efficient and effortless analytics without relying on any particular algorithm.