Publicação
Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis
| Resumo: | ML pipelines are composed of several steps that load data, clean it, process it, apply learning algorithms and produce either reports or deploy inference systems into production. In real-world scenarios, pipelines can take days, weeks, or months to train with large quantities of data. Unfortunately, current tools to design and orchestrate ML pipelines are oblivious to the semantics of each step, allowing developers to easily introduce errors when connecting two components that might not work together, either syntactically or semantically. Data scientists and engineers often find these bugs during or after the lengthy execution, which decreases their productivity. We propose a Visual Programming Language (VPL) enriched with semantic constraints regarding the behavior of each component and a verification methodology that verifies entire pipelines to detect common ML bugs that existing visual and textual programming languages do not. We evaluate this methodology on a set of six bugs taken from a data science company focused on preventing financial fraud on big data. We were able detect these data engineering and data balancing bugs, as well as detect unnecessary computation in the pipelines. |
|---|---|
| Autores principais: | David, João Pedro Vieira |
| Assunto: | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866811393929379840 |
|---|---|
| author | David, João Pedro Vieira |
| author_facet | David, João Pedro Vieira |
| author_role | author |
| contributor_name_str_mv | Fonseca, Alcides Miguel Cachulo Aguiar Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"David, João Pedro Vieira\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Fonseca, Alcides Miguel Cachulo Aguiar Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | David, João Pedro Vieira |
| datacite.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-03-25T13:48:33Z |
| datacite.date.embargoed.fl_str_mv | 2022-03-25T13:48:33Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| datacite.titles.title.fl_str_mv | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| dc.contributor.none.fl_str_mv | Fonseca, Alcides Miguel Cachulo Aguiar Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | David, João Pedro Vieira |
| dc.date.Accepted.fl_str_mv | 2021-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-03-25T13:48:33Z |
| dc.date.embargoed.fl_str_mv | 2022-03-25T13:48:33Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/51973 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| dc.title.fl_str_mv | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | ML pipelines are composed of several steps that load data, clean it, process it, apply learning algorithms and produce either reports or deploy inference systems into production. In real-world scenarios, pipelines can take days, weeks, or months to train with large quantities of data. Unfortunately, current tools to design and orchestrate ML pipelines are oblivious to the semantics of each step, allowing developers to easily introduce errors when connecting two components that might not work together, either syntactically or semantically. Data scientists and engineers often find these bugs during or after the lengthy execution, which decreases their productivity. We propose a Visual Programming Language (VPL) enriched with semantic constraints regarding the behavior of each component and a verification methodology that verifies entire pipelines to detect common ML bugs that existing visual and textual programming languages do not. We evaluate this methodology on a set of six bugs taken from a data science company focused on preventing financial fraud on big data. We were able detect these data engineering and data balancing bugs, as well as detect unnecessary computation in the pipelines. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/7346f002-c5d7-4e20-af67-b97423763193/download |
| id | ul_de819dc894a365dfbc5d2f541c6fef8a |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/51973 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/51973 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | David, João Pedro Vieira |
| publishDate | 2021 |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engpt_PTML pipelines are composed of several steps that load data, clean it, process it, apply learning algorithms and produce either reports or deploy inference systems into production. In real-world scenarios, pipelines can take days, weeks, or months to train with large quantities of data. Unfortunately, current tools to design and orchestrate ML pipelines are oblivious to the semantics of each step, allowing developers to easily introduce errors when connecting two components that might not work together, either syntactically or semantically. Data scientists and engineers often find these bugs during or after the lengthy execution, which decreases their productivity. We propose a Visual Programming Language (VPL) enriched with semantic constraints regarding the behavior of each component and a verification methodology that verifies entire pipelines to detect common ML bugs that existing visual and textual programming languages do not. We evaluate this methodology on a set of six bugs taken from a data science company focused on preventing financial fraud on big data. We were able detect these data engineering and data balancing bugs, as well as detect unnecessary computation in the pipelines.application/pdfpt_PTImproving Machine Learning Pipeline Creation using Visual Programming and Static AnalysisDavid, João Pedro VieiraFonseca, Alcides Miguel Cachulo AguiarHostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptURNurn:tid:2029340712022-03-25T13:48:33Z202120212021-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/51973http://purl.org/coar/access_right/c_abf2open accessProgramação VisualAprendizagem AutomáticaPipelineVerificação de TiposCompiladorTeses de mestrado - 20211995033 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/7346f002-c5d7-4e20-af67-b97423763193/download |
| spellingShingle | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis David, João Pedro Vieira Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| status | SINGLETON |
| subject.fl_str_mv | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| title | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| title_full | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| title_fullStr | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| title_full_unstemmed | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| title_short | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| title_sort | Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis |
| topic | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| topic_facet | Programação Visual Aprendizagem Automática Pipeline Verificação de Tipos Compilador Teses de mestrado - 2021 |
| url | http://hdl.handle.net/10451/51973 |
| visible | 1 |