Applying DevOps to Machine Learning Processes: A Systematic Mapping

  • Beatriz Mayumi Andrade Matsui UFABC
  • Denise Hideko Goya UFABC

Resumo


Práticas de DevOps têm sido cada vez mais utilizadas por equipes de engenharia de software com o intuito de aprimorar as etapas de desenvolvimento. Em processos que envolvem machine learning (ML), DevOps também pode ser aplicado a fim de implantar modelos de aprendizado de máquina em produção – prática também conhecida como MLOps. Neste mapeamento sistemático objetiva-se entender como DevOps tem sido aplicado a processos de machine learning e quais são os desafios enfrentados. Foram selecionados 15 artigos e observou-se que a maioria faz uso de práticas de CI/CD e propõe arquiteturas para a implantação de modelos de ML. Como maiores desafios, têm-se as características inerentes aos modelos de ML e resistência à mudança.

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Publicado
29/11/2021
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MATSUI, Beatriz Mayumi Andrade; GOYA, Denise Hideko. Applying DevOps to Machine Learning Processes: A Systematic Mapping. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 559-570. DOI: https://doi.org/10.5753/eniac.2021.18284.