Make or buy strategy for Machine Learning Operations - MLOps

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dc.contributor.authorNogare D.
dc.contributor.authorSilveira I.F.
dc.contributor.authorBanzai R.
dc.contributor.authorAlexandre M.C.
dc.date.accessioned2025-06-01T06:13:57Z
dc.date.available2025-06-01T06:13:57Z
dc.date.issued2025
dc.description.abstract© 2025 Departamento de Enfermagem/Universidade Federal de Sao Paulo. All rights reserved.This research addresses the make or buy strategy for Machine Learning Operations (MLOps), exploring the decision between developing internally or purchasing computational solutions for Machine Learning projects. Considering factors such as cost, quality, technical expertise and strategic alignment, organizations face the challenge of balancing product complexity, core competencies and risk management. This research highlights the importance of understanding the needs of each project when analyzing existing offers to solve problems and maintain competitiveness in the market, offering a guide for drive and support your decision. Additionally, qualitative and quantitative reviews of MLFlow, Airflow, Kubeflow, Databricks, Dataiku, H2O, Amazon AWS, Microsoft Azure, and Google GCP tools are presented, which facilitate the life-cycle management of machine learning models. This research contributes to the understanding of the challenges and strategies involved in the effective implementation of MLOps projects.
dc.description.issuenumber2
dc.description.volume97
dc.identifier.doi10.1590/0001-3765202520240924
dc.identifier.issnNone
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/40901
dc.relation.ispartofACTA Paulista de Enfermagem
dc.rightsAcesso Restrito
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguageMachine Learning Operations
dc.subject.otherlanguagemake or buy
dc.subject.otherlanguagemake or buy strategy
dc.subject.otherlanguageMLOps
dc.subject.otherlanguagemodel life-cycle
dc.titleMake or buy strategy for Machine Learning Operations - MLOps
dc.typeArtigo
local.scopus.citations0
local.scopus.eid2-s2.0-105005474820
local.scopus.subjectHumans
local.scopus.subjectMachine Learning
local.scopus.updated2025-06-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105005474820&origin=inward
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