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

Kennedy Ochilo Hadullo,Daniel Makini Getuno

doi:10.3844/ajassp.2021.152.164

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.

Highlights

Artificial Intelligence(AI) has become an important area of research in the 21st Century in many fields including: Marketing, education, banking, finance, agriculture, healthcare, space exploration, autonomous vehicles, law and so forth (Keshari, 2020; Hull, 2020)
The AI domain consists of several subfields, such as Machine Learning (ML), Deep Learning (DL), natural language processing, image processing and data mining which are important topics in computing research and technology industries (Zhang and Tsai, 2005; Zhang et al, 2019)
In order to answer the study objectives, we propose to come up with a Software Design Architecture (SDA) to better understand the basic structure of a ML software and a Software Deployment Workflow (SDW) to guide the development and deployment of ML software and help overcome the challenges identified in the study

Summary

Introduction

Artificial Intelligence(AI) has become an important area of research in the 21st Century in many fields including: Marketing, education, banking, finance, agriculture, healthcare, space exploration, autonomous vehicles, law and so forth (Keshari, 2020; Hull, 2020). The AI domain consists of several subfields, such as Machine Learning (ML), Deep Learning (DL), natural language processing, image processing and data mining which are important topics in computing research and technology industries (Zhang and Tsai, 2005; Zhang et al, 2019). Despite the interest caused by ML due to its wide applications and benefits in computing technology, DL, a subfield of machine learning is attracting much attention as well. Despite the potential created by both ML and DL in data science projects, there is evidence that majority of the projects do not make it to production (Redapt Marketing, 2019; Ameisen, 2020) with a high failure rate of approximately up to 90% being reported.

Motivation

Background

Literature Review

Use of REST API

Related Work

Design TD

DL software deployment model

Summary of Literature Review

Findings

Conclusion and Recommendations