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

Raj M Shukla; John P Cartlidge

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Publication status	Accepted/In press - 30 Oct 2021
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/

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/

Keywords

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

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Claritum KTP - 11952
Cartlidge, J. P.
22/08/19 → 8/09/22
Project: Research

Cite this

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title = "AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement",

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.",

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

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). Institute of Electrical and Electronics Engineers (IEEE), 2021.

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

TY - GEN

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 - 2021/10/30

Y1 - 2021/10/30

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

M3 - Conference Contribution (Conference Proceeding)

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

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 -

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

Abstract

Conference

Keywords

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Claritum KTP - 11952

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