pyStudio: An Open-Source Machine Learning Platform

Gomicia Murcia, Enrique ORCID: https://orcid.org/0009-0005-0396-9575, Bordel Sánchez, Borja ORCID: https://orcid.org/0000-0001-7815-5924, Souissi, Riad ORCID: https://orcid.org/0000-0002-3793-5585 and AL-Qurishi, Muhammad ORCID: https://orcid.org/0000-0002-7594-7325 (2024). pyStudio: An Open-Source Machine Learning Platform. En: "15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023)", 06-09 Nov 2023, Kusadasi, Turquía. ISBN 979-8-4007-0409-3. pp. 436-440. https://doi.org/10.1145/3625007.3632288.

Descripción

Título: pyStudio: An Open-Source Machine Learning Platform
Autor/es:
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 15th IEEE/ACM Annual International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023)
Fechas del Evento: 06-09 Nov 2023
Lugar del Evento: Kusadasi, Turquía
Título del Libro: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Título de Revista/Publicación: PROCEEDINGS OF THE 2023 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, ASONAM 2023
Fecha: 14 Marzo 2024
ISBN: 979-8-4007-0409-3
ISSN: 24739928
Materias:
Escuela: E.T.S.I. de Sistemas Informáticos (UPM)
Departamento: Sistemas Informáticos
Licencias Creative Commons: Ninguna

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Resumen

Data analytics has emerged as a critical capability for businesses and organizations in the modern era. The abundance of data necessitates a deep understanding and the exploitation of its potential to gain insights into current and future scenarios. This paper introduces an integrated platform designed to streamline data acquisition, storage, management, processing, and visualization. The primary objective is to facilitate data analysis by offering a machine learning studio equipped with pre-built algorithms. Remarkably, this platform eliminates the need for coding, allowing users to effortlessly generate AI models. Furthermore [19], it provides a secure environment for sharing these models without compromising data privacy-a noteworthy contribution in the realm of federated learning (FL). The platform's significance lies in its ability to empower non-technical users to perform advanced tasks without requiring specialized expertise.

Más información

ID de Registro: 85693
Identificador DC: https://oa.upm.es/85693/
Identificador OAI: oai:oa.upm.es:85693
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/10207879
Identificador DOI: 10.1145/3625007.3632288
URL Oficial: https://dl.acm.org/doi/10.1145/3625007.3632288
Depositado por: iMarina Portal Científico
Depositado el: 14 Ene 2025 17:43
Ultima Modificación: 14 Ene 2025 17:43