AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Machine learning (ML) project deployments often have long lead times and may face delays or failures due to lack of data, poor data quality, and data drift. To address these problems, we introduce AgileML, a novel machine learning product development lifecycle where the end consumer and development team work collaboratively through an iterative process of development. We use AgileML to develop a commercial spend classification service and demonstrate that the earliest alpha deployment can offer users significant commercial value. User-testing with a professional spend analyst demonstrates that the system can lead to a five-fold increase in classification speed.
Original languageEnglish
Title of host publication2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusAccepted/In press - 30 Oct 2021
EventThe Asia-Pacific Conference on Computer Science and Data Engineering - Virtual, Brisbane, Australia
Duration: 8 Dec 202110 Dec 2021
Conference number: 8
https://ieee-csde.org/2021/

Conference

ConferenceThe Asia-Pacific Conference on Computer Science and Data Engineering
Abbreviated titleIEEE CSDE 2021
Country/TerritoryAustralia
CityBrisbane
Period8/12/2110/12/21
Internet address

Keywords

  • Spend management
  • AI service
  • Model deployment
  • ML development pipeline
  • Support vector machine

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