Machine Learning Application Development: Practitioners' Insights

TL;DR

This paper presents a survey of 80 practitioners to identify key challenges and best practices in developing machine learning applications, aiming to guide both industry and research efforts.

Contribution

It provides a comprehensive synthesis of challenges and best practices in ML application development based on practitioner insights, highlighting areas for future research.

Findings

Identified 17 key challenges and best practices.

Practitioners emphasize data quality and model interpretability.

Industry practices vary across domains and experience levels.

Abstract

Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML). However, machine learning meets software engineering not only with promising potentials but also with some inherent challenges. Despite some recent research efforts, we still do not have a clear understanding of the challenges of developing ML-based applications and the current industry practices. Moreover, it is unclear where software engineering researchers should focus their efforts to better support ML application developers. In this paper, we report about a survey that aimed to understand the challenges and best practices of ML application development. We synthesize the results obtained from 80 practitioners (with diverse skills, experience, and application domains) into 17 findings; outlining challenges and best practices for ML application development. Practitioners involved in the development of ML-based software systems can leverage the summarized best practices to improve the quality of their system. We hope that the reported challenges will inform the research community about topics that need to be investigated to improve the engineering process and the quality of ML-based applications.