Toward efficient data science: A comprehensive MLOps template for collaborative code development and automation

In the era of big data analytics and AI applications, data provenance is as important as ever, particularly as applications emerge in vital industries like healthcare. Additionally, as the suites of tools and packages grow exponentially, code transparency and experiment record keeping are essential...

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Bibliographic Details
Published inSoftwareX Vol. 26; p. 101723
Main Authors Godwin, Ryan C., Melvin, Ryan L.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2024
Elsevier
Subjects
Artificial intelligence
Machine learning
Machine learning operations
Software reliability
Software template
Traceability
Traceability
Software reliability
Machine learning operations
Software template
Artificial intelligence
Machine learning
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Summary:In the era of big data analytics and AI applications, data provenance is as important as ever, particularly as applications emerge in vital industries like healthcare. Additionally, as the suites of tools and packages grow exponentially, code transparency and experiment record keeping are essential to ensuring full traceability of AI and ML models. This manuscript presents an open-source Machine Learning Operations (MLOps) Template that provides a consistent framework to support collaborative development and improve efficiency. The template provides a robust and reliable software structure incorporating essential development aspects. These tools include automated code documentation, built-in package management, experiment tracking, configuration and logging infrastructure, and more. The template is built on an agglomeration of best practices gleaned from industry and academia alike, providing a great starting point for any ML/AI project.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2024.101723