Machine Learning a Systems Engineering Perspective

Systems engineering seeks to understand the big picture by breaking complex projects into manageable well-defined sub-systems. This article will leverage fundamental systems engineering principles to introduce Machine Learning as a system composed of interacting elements. The usage of terminology throughout this article is an elaboration of the fundamental idea that a system is a purposeful whole consisting of interacting parts. Each element that is part of these system is atomic (i.e., not further decomposable) in nature and modeled using descriptive features. This reduces the complexity and supports task independence by allowing management authorities to get a high-level perspective of a machine learning pipeline.