Online platform for building, testing and deploying predictive models
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/45651 |
Resumo: | Machine Learning (ML) and Artificial Intelligence (AI) have been traditionally built and deployed manually in a single machine, using tools such as R or Weka. Times are changing and in the real-time service and big data era, this methods are being obsoleted, as they severely limit the applicability and deployability of ML. Many companies such as Microsoft, Amazon and Google have been trying to mitigate this problem developing their MLaaS (Machine Learning as a Service) solutions, which are online platforms capable to scale and automate the development of predictive models. Despite the existence of some ML platforms available in the cloud, that enable the user to develop and deploy ML processes, they are not suitable for rapidly prototype and deploy predictive models, as some complex steps need to be done before the user starts using them, like configuration of environments, configuration of accounts and the overcome of the steep learning curve. In this research project, it’s presented MLINO, which is a concept of an online platform that allows the user to rapidly prototype and deploy basic ML processes, in an intuitive and easy way. Even though the implementation of the prototype wasn’t the optimal, due to software and infrastructure limitations, through a series of experiments it was demonstrated that the final performance of the prototype was satisfactory. When benchmarking the devised solution against the Microsoft Azure ML, the results showed that MLINO tool is easier to use, and takes less time when building and deploying a basic predictive model. |
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Online platform for building, testing and deploying predictive modelsMachine learningPredictive modelsOnline platformFlexible systemSupervised-learningClassificationDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaMachine Learning (ML) and Artificial Intelligence (AI) have been traditionally built and deployed manually in a single machine, using tools such as R or Weka. Times are changing and in the real-time service and big data era, this methods are being obsoleted, as they severely limit the applicability and deployability of ML. Many companies such as Microsoft, Amazon and Google have been trying to mitigate this problem developing their MLaaS (Machine Learning as a Service) solutions, which are online platforms capable to scale and automate the development of predictive models. Despite the existence of some ML platforms available in the cloud, that enable the user to develop and deploy ML processes, they are not suitable for rapidly prototype and deploy predictive models, as some complex steps need to be done before the user starts using them, like configuration of environments, configuration of accounts and the overcome of the steep learning curve. In this research project, it’s presented MLINO, which is a concept of an online platform that allows the user to rapidly prototype and deploy basic ML processes, in an intuitive and easy way. Even though the implementation of the prototype wasn’t the optimal, due to software and infrastructure limitations, through a series of experiments it was demonstrated that the final performance of the prototype was satisfactory. When benchmarking the devised solution against the Microsoft Azure ML, the results showed that MLINO tool is easier to use, and takes less time when building and deploying a basic predictive model.Cardoso, TiagoRUNInfante, Gonçalo Brito2018-09-03T10:52:56Z2017-1220172017-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/45651enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:24:16Zoai:run.unl.pt:10362/45651Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:31:54.657813Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Online platform for building, testing and deploying predictive models |
title |
Online platform for building, testing and deploying predictive models |
spellingShingle |
Online platform for building, testing and deploying predictive models Infante, Gonçalo Brito Machine learning Predictive models Online platform Flexible system Supervised-learning Classification Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
Online platform for building, testing and deploying predictive models |
title_full |
Online platform for building, testing and deploying predictive models |
title_fullStr |
Online platform for building, testing and deploying predictive models |
title_full_unstemmed |
Online platform for building, testing and deploying predictive models |
title_sort |
Online platform for building, testing and deploying predictive models |
author |
Infante, Gonçalo Brito |
author_facet |
Infante, Gonçalo Brito |
author_role |
author |
dc.contributor.none.fl_str_mv |
Cardoso, Tiago RUN |
dc.contributor.author.fl_str_mv |
Infante, Gonçalo Brito |
dc.subject.por.fl_str_mv |
Machine learning Predictive models Online platform Flexible system Supervised-learning Classification Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
Machine learning Predictive models Online platform Flexible system Supervised-learning Classification Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Machine Learning (ML) and Artificial Intelligence (AI) have been traditionally built and deployed manually in a single machine, using tools such as R or Weka. Times are changing and in the real-time service and big data era, this methods are being obsoleted, as they severely limit the applicability and deployability of ML. Many companies such as Microsoft, Amazon and Google have been trying to mitigate this problem developing their MLaaS (Machine Learning as a Service) solutions, which are online platforms capable to scale and automate the development of predictive models. Despite the existence of some ML platforms available in the cloud, that enable the user to develop and deploy ML processes, they are not suitable for rapidly prototype and deploy predictive models, as some complex steps need to be done before the user starts using them, like configuration of environments, configuration of accounts and the overcome of the steep learning curve. In this research project, it’s presented MLINO, which is a concept of an online platform that allows the user to rapidly prototype and deploy basic ML processes, in an intuitive and easy way. Even though the implementation of the prototype wasn’t the optimal, due to software and infrastructure limitations, through a series of experiments it was demonstrated that the final performance of the prototype was satisfactory. When benchmarking the devised solution against the Microsoft Azure ML, the results showed that MLINO tool is easier to use, and takes less time when building and deploying a basic predictive model. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12 2017 2017-12-01T00:00:00Z 2018-09-03T10:52:56Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/45651 |
url |
http://hdl.handle.net/10362/45651 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137941215248384 |