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

Raj M Shukla; John P Cartlidge

doi:10.1109/CSDE53843.2021.9718470

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

Raj M Shukla, John P Cartlidge

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

3 Citations (Scopus)

135 Downloads (Pure)

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 language	English
Title of host publication	2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	978-1-6654-9552-3
ISBN (Print)	978-1-6654-9553-0
DOIs	https://doi.org/10.1109/CSDE53843.2021.9718470
Publication status	E-pub ahead of print - 1 Mar 2022
Event	The Asia-Pacific Conference on Computer Science and Data Engineering - Virtual, Brisbane, Australia Duration: 8 Dec 2021 → 10 Dec 2021 Conference number: 8 https://ieee-csde.org/2021/

Publication series

Name	2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021

Conference

Conference	The Asia-Pacific Conference on Computer Science and Data Engineering
Abbreviated title	IEEE CSDE 2021
Country/Territory	Australia
City	Brisbane
Period	8/12/21 → 10/12/21
Internet address	https://ieee-csde.org/2021/

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by Innovate UK Knowledge Transfer Partnership between University of Bristol and Claritum Limited (KTP 11952).

Publisher Copyright:
© IEEE 2022.

Keywords

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

Access to Document

10.1109/CSDE53843.2021.9718470

Full-text PDF (author’s accepted manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/9718470. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 552 KB

Handle.net

Persistent link

Claritum KTP - 11952
Cartlidge, J. (Principal Investigator)
22/08/19 → 31/01/23
Project: Research

Cite this

Shukla, R. M., & Cartlidge, J. P. (2022). AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021 (2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021). Institute of Electrical and Electronics Engineers (IEEE). Advance online publication. https://doi.org/10.1109/CSDE53843.2021.9718470

Shukla, Raj M ; Cartlidge, John P. / AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021. Institute of Electrical and Electronics Engineers (IEEE), 2022. (2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021).

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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.",

keywords = "Spend management, AI service, Model deployment, ML development pipeline, Support vector machine",

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note = "Funding Information: ACKNOWLEDGMENT This work was supported by Innovate UK Knowledge Transfer Partnership between University of Bristol and Claritum Limited (KTP 11952). Publisher Copyright: {\textcopyright} IEEE 2022.; The Asia-Pacific Conference on Computer Science and Data Engineering, IEEE CSDE 2021 ; Conference date: 08-12-2021 Through 10-12-2021",

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Shukla, RM & Cartlidge, JP 2022, AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement. in 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Institute of Electrical and Electronics Engineers (IEEE), The Asia-Pacific Conference on Computer Science and Data Engineering, Brisbane, Australia, 8/12/21. https://doi.org/10.1109/CSDE53843.2021.9718470

AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement. / Shukla, Raj M; Cartlidge, John P.
2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021. Institute of Electrical and Electronics Engineers (IEEE), 2022. (2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021).

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

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T1 - AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement

AU - Shukla, Raj M

AU - Cartlidge, John P

N1 - Conference code: 8

PY - 2022/3/1

Y1 - 2022/3/1

N2 - 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.

AB - 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.

KW - Spend management

KW - AI service

KW - Model deployment

KW - ML development pipeline

KW - Support vector machine

U2 - 10.1109/CSDE53843.2021.9718470

DO - 10.1109/CSDE53843.2021.9718470

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-6654-9553-0

T3 - 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021

BT - 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - The Asia-Pacific Conference on Computer Science and Data Engineering

Y2 - 8 December 2021 through 10 December 2021

ER -

Shukla RM, Cartlidge JP. AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021. Institute of Electrical and Electronics Engineers (IEEE). 2022. (2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021). Epub 2022 Mar 1. doi: 10.1109/CSDE53843.2021.9718470

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Handle.net

Fingerprint

Projects

Claritum KTP - 11952

Cite this