Systematic Mapping Study on the Machine Learning Lifecycle

Yuanhao Xie, Luís Cruz, Petra Heck, Jan S. Rellermeyer

Research output: Chapter in Book/Conference proceedings/Edited volumeEntry for encyclopedia/dictionaryScientificpeer-review


The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
Original languageEnglish
Title of host publication2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)
EditorsL. O'Conner
Place of PublicationPiscataway
Number of pages4
ISBN (Electronic)978-1-6654-4470-5
ISBN (Print)978-1-6654-4471-2
Publication statusPublished - 2021
EventWAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI - Virtual, Madrid, Spain
Duration: 30 May 202131 May 2021


ConferenceWAIN'21 - 1st Workshop on AI Engineering – Software Engineering for AI
Abbreviated titleWAIN'21
Internet address


  • AI lifecycle management
  • Artificial Intelligence
  • Software Engineering
  • Systematic mapping study


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