The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice
This work addresses the problem of streamlining AI development pipelines for companies, but it is incremental as it synthesizes existing research rather than introducing new methods.
The paper tackles the challenge of continuously developing and deploying AI models in production by conducting a multivocal literature review and interviews to consolidate terminology, triggers, tasks, and challenges in AI pipelines, resulting in a taxonomy with four stages: Data Handling, Model Learning, Software Development, and System Operations.
Companies struggle to continuously develop and deploy AI models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required. This paper includes a Multivocal Literature Review where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle management, and CD4ML. Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.