Multivocal Literature Review &amp; Interviews: Continuous End-to-End Lifecycle Management Pipeline for Artificial Intelligence

During a multivocal literature review 151 relevant formal and informal sources were extracted. Additional information was collected from nine semi-structured interviews. The extracted dataset provides the foundation for the presentation and comparison of different terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle management, and CD4ML. Furthermore, the dataset comprises potential triggers for reiterating the pipeline and consolidated tasks necessary in a continuous end-to-end lifecycle pipeline categorized into four stages: Data, Model, Dev and Ops. Additionally, the dataset provides information regarding challenges of the lifecycle pipelines for AI.

Related Organizations

Software Competence Center Hagenberg (Austria)
Austria
University of Innsbruck
Austria

Keywords

Continuous Integration for AI, Continuous Deployment for AI, MLOps, CD4ML, Continuous Delivery for AI, CI/CD for AI, DevOps for AI, continuous end-to-end lifecycle management pipelines for AI

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average