Defesa de Monografia - "Learning Orchestra: building ML workflows on the cloud"

Segue convite para assistirem a 3a defesa remota de monografia nesta época de pandemia. Será a defesa do trabalho desenvolvido pelo discente Gabriel de Oliveira Ribeiro na disciplina BCC392 (Monografia I). Aos que puderem participar, entrem na sala do Meet com o microfone mudo e a câmera desativada, no intuito de não comprometerem a qualidade da defesa.

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Data e horário: 09/10 (sexta-feira) às 13h30.

Sala do Meet: meet.google.com/bia-oiuw-omo

Título: Learning Orchestra: building ML workflows on the cloud

Resumo:
Interesting efforts were done to construct tools to facilitate and streamline the development of Machine Learning (ML) workflows composed of several pipelines in the last two decades. From Unix scripts to Web based ML components and solutions to automate and orchestrate ML and Data Mining (DM) pipes, we have tried many high level services for the data scientist iterative process. On the other hand, we have the low level services being investigated, like cloud environments, container orchestration, fault tolerance service and so forth. Normally, scripts are produced to simplify such low level services operations. Unfortunately, no existing solution put both low and high level services on a unique service stack. Furthermore, none of them enables the utilization of different existing tools during the construction of a single pipeline, i.e., they are not flexible enough to permit a tool to build pre-processing pipes, another tool to build parameters tuning steps and a third different tool to perform the training step of a single pipeline. To address these limitations, we present the Learning Orchestra system, a tool to construct complex workflows using different ML tools or players transparently, i.e., from a single interoperable API we can build interesting analytical flows. The workflows can be deployed on a containerized cloud environment capable to scale and be resilient. Initial experiments demonstrated that our system is a promising and innovative alternative for the problem of simplify and streamline the ML iterative process.

Banca:
- Dr. Joubert de Castro Lima (orientador - UFOP)
- MSc. Lauro Ângelo Gonçalves de Moraes (coorientador - UFOP)
- Dr. Eduardo José da Silva Luz (examinador - UFOP)

Departamento de Computação  |  ICEB  |  Universidade Federal de Ouro Preto
Campus Universitário Morro do Cruzeiro  |  CEP 35400-000  |  Ouro Preto - MG, Brasil
Telefone: +55 31 3559-1692  |  decom@ufop.edu.br