A microservice based architecture topology for machine learning deployment

José Lucas Ribeiro, Mickael Figueredo, Adelson Araujo jr., Nélio Cacho, Frederico Lopes

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


Smart solutions that make use of machine learning and data analyses are on the rise. Big Data analysis is attracting more and more developers and researchers, and at least five requirements (Velocity, Volume, Value, Variety, and Veracity) show challenges in deploying such solutions. Across the globe, many Smart City initiatives are using Big Data Analytics as a tool for doing predictive analytics which can be helpful to human well being. This work presents a generic architecture named Machine Learning in Microservices Architecture (MLMA) that provides design patterns to transform a monolithic architecture of machine learning pipelines in microservices with separate roles. We present two case studies deployed to a Smart City initiative, where we discuss how each component of the architecture applied in specific applications that use predictions with machine learning. Among the benefits of this architecture, we argue prediction performance, scalability, code maintenance and reusability makes such transition a natural trend in Big Data and machine learning applications.
Original languageEnglish
Title of host publication2019 IEEE International Smart Cities Conference (ISC2)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)978-1-7281-0846-9
ISBN (Print)978-1-7281-0845-2, 978-1-7281-0847-6
Publication statusPublished - 2019
Event2019 IEEE International Smart Cities Conference, ISC2 2019 - Casablanca, Morocco
Duration: 14 Oct 201917 Oct 2019

Publication series

NameIEEE International Smart Cities Conference (ISC2)
ISSN (Print)2687-8852
ISSN (Electronic)2687-8860


Conference2019 IEEE International Smart Cities Conference, ISC2 2019
Abbreviated titleISC2 2019


  • Microservices
  • Machine learning
  • Design patterns
  • Recommendation systems
  • Predictive policing


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