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Sökning: onr:"swepub:oai:research.chalmers.se:b5d7bfba-0779-4e01-88b6-fc7e45dba585" > Architecting AI Dep...

Architecting AI Deployment: A Systematic Review of State-of-the-Art and State-of-Practice Literature

John, Meenu Mary (författare)
Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Software Center
Olsson, Helena Holmström (författare)
Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Software Center
Bosch, Jan, 1967 (författare)
Chalmers tekniska högskola,Chalmers University of Technology,Software Center
 (creator_code:org_t)
2021-01-22
2021
Engelska.
Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 407, s. 14-29, s. 14-29
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • Companies across domains are rapidly engaged in shifting computational power and intelligence from centralized cloud to fully decentralized edges to maximize value delivery, strengthen security and reduce latency. However, most companies have only recently started pursuing this opportunity and are therefore at the early stage of the cloud-to-edge transition. To provide an overview of AI deployment in the context of edge/cloud/hybrid architectures, we conduct a systematic literature review and a grey literature review. To advance understanding of how to integrate, deploy, operationalize and evolve AI models, we derive a framework from existing literature to accelerate the end-to-end deployment process. The framework is organized into five phases: Design, Integration, Deployment, Operation and Evolution. We make an attempt to analyze the extracted results by comparing and contrasting them to derive insights. The contribution of the paper is threefold. First, we conduct a systematic literature review in which we review the contemporary scientific literature and provide a detailed overview of the state-of-the-art of AI deployment. Second, we review the grey literature and present the state-of-practice and experience of practitioners while deploying AI models. Third, we present a framework derived from existing literature for the end-to-end deployment process and attempt to compare and contrast SLR and GLR results.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik -- Övrig annan teknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies -- Other Engineering and Technologies not elsewhere specified (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)
SAMHÄLLSVETENSKAP  -- Medie- och kommunikationsvetenskap -- Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning (hsv//swe)
SOCIAL SCIENCES  -- Media and Communications -- Information Systems, Social aspects (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Challenges
Deep learning
Grey literature review
Practices
Deployment
Systematic literature review
Machine learning

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