Reference architecture and cost estimation model for building intelligent platforms

Abstract

With the recent resurgence of Machine Learning and Artificial Intelligence as a competitive advantage in product development, technical executives and managers are interested in learning what it would take to build intelligent platforms that can leverage these advances. In addition, they wish to produce cost estimates for developing such platforms. The goal of this thesis is to develop a reference architecture for an intelligent platform and an associated costing model that allows technical managers to understand the components needed to deliver such a platform and estimate the cost of each module, estimate the cost of the overall architecture, and enable what-if analysis to understand the cost tradeoffs. The intent is not to provide the values of the variables in the model, but to develop a cost model that will enable interested parties to plug in their estimated values for each factor and generate a forecast of the build cost.