Machine Learning Software Architecture and Model Workflow. A Case of Django REST Framework

Abstract

The purpose of this study was to find out the challenges facing Machine Learning (ML) software development and create a design architecture and a workflow for successful deployment. Despite the promise in ML technology, more than 80% of ML software projects never make it to production. As a result, majority of companies around the world with investments in ML software are making significant losses. Current studies show that data scientists and software engineers are concerned by the challenges involved in these systems such as: limited qualified and experienced ML software experts, lack of collaboration between experts from the two domains, lack of published literature in ML software development using established platforms such as Django Rest Framework, as well as existence of cloud software tools that are difficult use. Several attempts have been made to address these issues such as: Coming up with new software models and architectures, frameworks and design patterns. However, with the lack of a clear breakthrough in overcoming the challenges, this study proposes to investigate further into the conundrum with the view of proposing an ML software design architecture and a development workflow. In the end, the study gives a conclusion on how the remedies provided helps to meet the objectives of study.