  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.
