Year: 2025
Type: article
Abstract: ABSTRACT Machine Learning (ML) development introduces challenges that traditional software processes often struggle to address. As ML applications grow in complexity and adoption, various lifecycle models have been proposed to address the unique stages of ML development. This study systematically synthesises these models, mapping their stages and activities to provide an understanding of the ML development landscape. The findings highlight research gaps and opportunities, offering insights for advancing academic research and practical implementation.
Source: Expert Systems
DOI: 10.1111/exsy.70029
Institution: Universitat de les Illes Balears
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,600
OpenAlex ID: W4408374043