Benefits of Layered Software Architecture in Machine Learning Applications

Abstract

The benefits of layering in software applications are well-known not only to authors and industry experts, but to software enthusiasts as well because the layering provides a testable and more error-proof framing for applications. Despite the benefits, however, the increasingly popular area of machine learning is yet to embrace the advantages of such a design. In the present paper, we aim to investigate if characteristic benefits of layered architecture can be applied to machine learning by designing and building a system that uses a layered machine learning approach. Then, the implemented system is compared to other already existing implementations in the literature targeting the field of facial recognition. Although we chose this field as our example for its literature being rich in both theoretical foundations and practical implementations, the principles and practices outlined by the present work are also applicable in a more general sense.