Design patterns for Machine Learning Applications

The aim of this paper is detecting and analyzing design patterns and architectural patterns for two software applications that use Machine Learning (ML) and Deep Learning techniques respectively. The classification is done based on the design principles that need to be adhered for a standard design and architectural patterns. ML based applications generally have ubiquitous modules to some extent. However, modeling their components through varied design patterns bring out positive changes to the performance of the systems as well as mitigates many of the computational shortcomings faced otherwise. Although it is still a novel approach for systems implementing machine learning algorithms, the paper aims to bring a new paradigm in analyzing system models.