The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice

TL;DR

This paper reviews the current state of continuous AI development pipelines, consolidating terminology, triggers, tasks, and challenges through literature review and interviews, and proposes a structured pipeline with four key stages.

Contribution

It provides a comprehensive taxonomy and comparison of terminologies, triggers, and challenges in continuous AI development pipelines, integrating insights from literature and industry experts.

Findings

Consolidated terminology for DevOps, MLOps, and CI/CD in AI.

Identified key triggers for pipeline reiteration, such as alerts and schedules.

Proposed a four-stage AI development pipeline with mapped challenges.

Abstract

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.