{"id":33210,"date":"2025-05-27T13:23:03","date_gmt":"2025-05-27T11:23:03","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=33210"},"modified":"2025-05-27T13:30:21","modified_gmt":"2025-05-27T11:30:21","slug":"machine-learnig","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/es\/backend-es\/machine-learnig\/","title":{"rendered":"Building End-to-End ML Workflows in Python: From Data to Production"},"content":{"rendered":"\n<p>Hace una semana se celebr\u00f3 Codemotion Madrid 2025, los d\u00edas 20 y 21 de mayo en el espacio MEEU Madrid (Estaci\u00f3n de Chamart\u00edn). Fue un evento fant\u00e1stico, repleto de las \u00faltimas innovaciones tecnol\u00f3gicas.<\/p>\n\n\n\n<p>Una de las charlas m\u00e1s destacadas fue<strong> Building End-to-End ML Workflows in Python: From Data to Production,<\/strong> en la que se mostr\u00f3 c\u00f3mo implementar un flujo de trabajo completo de Machine Learning con Python, desde la ingesta y preparaci\u00f3n de datos hasta el despliegue en producci\u00f3n.<\/p>\n\n\n\n<p>La sesi\u00f3n, impartida en ingl\u00e9s por<strong><a href=\"http:\/\/linkedin.com\/in\/grace-adamson\/\" target=\"_blank\" rel=\"noreferrer noopener\"> Grace Adamson,<\/a> AI Senior Product Marketing Manager en Snowflake, tuvo lugar el mi\u00e9rcoles 21 a las 11:45 en el Plat\u00f3 2.<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-contexto-de-la-charla\">Contexto de la charla<\/h2>\n\n\n\n<p>El machine learning no se trata solo de construir un modelo, sino de <strong>crear un pipeline fiable y escalable desde los datos sin procesar hasta la inferencia en tiempo real<\/strong>. Cuando hablamos de un flujo de trabajo de Machine Learning hacemos referencia al workflow y procesos que permiten dise\u00f1ar, construir y desplegar un modelo de ML.<\/p>\n\n\n\n<p>El objetivo de cualquier pipeline de ML es automatizar estos procesos para crear <strong>modelos s\u00f3lidos, escalables y listos para pasar a producci\u00f3n<\/strong>. Sin embargo, alcanzar esta meta no es tan sencillo.<\/p>\n\n\n\n<p>De hecho, y seg\u00fan un estudio de <a href=\"https:\/\/www.datarobot.com\/product\/ai-platform\/?redirect_source=algorithmia.com&amp;ref=labellerr.com\">Datarobot<\/a>, <strong>el 55 % de las empresas interesadas en implementar un modelo de ML acaban por desechar la idea debido a la gran complejidad<\/strong> que supone administrar los flujos de trabajo de datos y los procesos de implementaci\u00f3n asociados.<\/p>\n\n\n\n<p>El dise\u00f1o y puesta en marcha de un pipeline de ML <strong>consta de varias etapas que implican diferentes roles y tecnolog\u00edas<\/strong>: desde la ingesta y limpieza de datos, pasando por el entrenamiento y optimizaci\u00f3n de modelos, hasta el despliegue y monitorizaci\u00f3n en producci\u00f3n.&nbsp;<\/p>\n\n\n\n<p>Cada una de estas fases requiere un buen conjunto de habilidades y herramientas, lo que hace que la gesti\u00f3n del pipeline completo sea un <strong>gran desaf\u00edo para muchas organizaciones<\/strong>. Sin embargo, esto es necesario si queremos que nuestro pipeline sea:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Reproducible<\/strong>: poder replicar todos los pasos del proceso de forma consistente, garantizando que los resultados sean los mismos cada vez que se ejecute el pipeline.<\/li>\n\n\n\n<li><strong>Escalable<\/strong>: debe ser capaz de manejar grandes vol\u00famenes de datos y adaptarse a las necesidades cambiantes del negocio sin comprometer el rendimiento.<\/li>\n\n\n\n<li><strong>Mantenible<\/strong>: f\u00e1cil de actualizar, monitorizar y depurar cuando sea necesario, con un c\u00f3digo limpio y bien documentado.<\/li>\n\n\n\n<li><strong>Simple:<\/strong> mantener el flujo de trabajo lo m\u00e1s sencillo posible, evitando complejidades innecesarias y facilitando su comprensi\u00f3n por parte de todo el equipo.<\/li>\n\n\n\n<li><strong>R\u00e1pido de implementar:<\/strong> poder desplegar modelos y pipelines r\u00e1pidamente en producci\u00f3n, minimizando el tiempo entre el desarrollo y la implementaci\u00f3n real.<\/li>\n\n\n\n<li><strong>Innovador:<\/strong> poder incorporar las \u00faltimas tecnolog\u00edas y mejores pr\u00e1cticas en ML, manteni\u00e9ndose al d\u00eda con las nuevas herramientas y metodolog\u00edas.<\/li>\n\n\n\n<li><strong>Reutilizable:<\/strong> poder aprovechar componentes y patrones probados en diferentes proyectos de ML, reduciendo el tiempo de desarrollo y mejorando la consistencia entre diferentes implementaciones.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-etapas-de-implementacion-de-un-pipeline-y-por-que-hacerlo-con-python\">Etapas de implementaci\u00f3n de un Pipeline y por qu\u00e9 hacerlo con Python<\/h2>\n\n\n\n<p><strong>Python<\/strong> ha demostrado ser uno de los mejores lenguajes para el desarrollo de pipelines de ML. Y lo es por varias razones: su extensa biblioteca de paquetes especializados, su simplicidad sint\u00e1ctica, su capacidad para manejar todo el ciclo de vida del desarrollo de ML, integraci\u00f3n sencilla con otras plataformas, etc.<\/p>\n\n\n\n<p>Su <strong>ecosistema rico en frameworks como TensorFlow, PyTorch y scikit-learn <\/strong>lo convierten en la elecci\u00f3n natural para implementar flujos de trabajo de ML completos y escalables.<\/p>\n\n\n\n<p>Python ofrece grandes ventajas a la hora de construir pipelines de ML, como su capacidad para <strong>manejar procesamiento paralelo<\/strong>, su integraci\u00f3n perfecta con <strong>herramientas de orquestaci\u00f3n como Apache Airflow o Kubeflow <\/strong>o su amplia gama de bibliotecas para automatizaci\u00f3n y monitorizaci\u00f3n. Estas caracter\u00edsticas, junto con su naturaleza interpretada y din\u00e1mica, permiten una iteraci\u00f3n r\u00e1pida durante las diferentes etapas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ingesta y preparaci\u00f3n de datos<\/strong>: recopilaci\u00f3n, limpieza y transformaci\u00f3n de datos brutos en un formato adecuado para el entrenamiento.<\/li>\n\n\n\n<li><strong>Selecci\u00f3n del modelo:<\/strong> es necesario elegir el algoritmo m\u00e1s adecuado para nuestros objetivos, teniendo en cuenta factores como el tipo de datos, la complejidad del problema y los requisitos de rendimiento.<\/li>\n\n\n\n<li><strong>Entrenamiento y validaci\u00f3n<\/strong>: proceso iterativo de construcci\u00f3n y refinamiento del modelo, incluyendo la selecci\u00f3n de caracter\u00edsticas y optimizaci\u00f3n de hiperpar\u00e1metros.<\/li>\n\n\n\n<li><strong>Evaluaci\u00f3n y pruebas:<\/strong> validaci\u00f3n del rendimiento del modelo contra m\u00e9tricas predefinidas y donde se realizan pruebas exhaustivas.<\/li>\n\n\n\n<li><strong>Despliegue y monitorizaci\u00f3n<\/strong>: etapa final donde el modelo se implementa en producci\u00f3n y se establece un sistema de seguimiento y mejora continua.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-la-charla-building-end-to-end-ml-workflows-in-python-from-data-to-production\">La charla: Building End-to-End ML Workflows in Python: From Data to Production<\/h2>\n\n\n\n<p>En la charla \u201cBuilding End-to-End ML Workflows in Python: From Data to Production\u201d, <strong>Grace Adamson nos ense\u00f1\u00f3 como crear un flujo de trabajo de ML completo en Python<\/strong>, dise\u00f1ado especialmente para desarrolladores, cient\u00edficos de datos e ingenieros de ML que quieren avanzar r\u00e1pidamente y construir sistemas listos para pasar a producci\u00f3n. Y todo ello sin necesidad de reinventar la rueda. Nuestros asistentes aprendieron c\u00f3mo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Preparar datos y dise\u00f1ar caracter\u00edsticas utilizando patrones consistentes y reutilizables para evitar la duplicaci\u00f3n y la deriva entre el entrenamiento y la inferencia.<\/li>\n\n\n\n<li>Entrenar y ajustar modelos con bibliotecas populares de c\u00f3digo abierto como scikit-learn, XGBoost y LightGBM, en CPU o GPU.<\/li>\n\n\n\n<li>Empaquetar y desplegar modelos para inferencia en tiempo real o por lotes con una sobrecarga operativa m\u00ednima.<\/li>\n\n\n\n<li>Realizar seguimiento de experimentos, monitorizar el rendimiento y depurar problemas con observabilidad integrada, seguimiento de linaje y explicabilidad del modelo.<\/li>\n<\/ul>\n\n\n\n<p>Se mostr\u00f3 c\u00f3mo todo esto puede realizarse dentro de un flujo de trabajo unificado usando Python, con la ayuda de <strong>entornos de ejecuci\u00f3n containerizados y herramientas integradas de versionado, orquestaci\u00f3n y despliegue<\/strong>. El objetivo: centrarnos en resolver problemas y no en gestionar la infraestructura.<\/p>\n\n\n\n<p>Esta fue una <strong>sesi\u00f3n pr\u00e1ctica y hands-on para desarrolladores<\/strong> que quieren pasar del notebook a producci\u00f3n sin soluciones improvisadas. Al finalizar, los asistentes se llevaron a casa un marco pr\u00e1ctico para construir sistemas de ML resilientes y escalables.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-la-ponente-impartida-en-ingles-por-grace-adamson-ai-senior-product-marketing-manager-en-snowflake\">La ponente: impartida en ingl\u00e9s por Grace Adamson, AI Senior Product Marketing Manager en Snowflake<\/h2>\n\n\n\n<p>L\u00edder de marketing de productos t\u00e9cnicos centrado en la IA, con pasi\u00f3n por traducir capacidades complejas en un valor convincente para el cliente.<\/p>\n\n\n\n<p>En Snowflake, dirige la estrategia de salida al mercado de la cartera de productos de IA, colaborando con los equipos de ingenier\u00eda, ventas y marketing para posicionar soluciones t\u00e9cnicas que resuelvan retos empresariales a todo el mundo, desde ejecutivos de alto nivel a analistas de datos.<\/p>\n\n\n\n<p>Anteriormente, ha pasado m\u00e1s de una d\u00e9cada como propietario de producto en SaaS, start ups y scale ups en la construcci\u00f3n, la salud y los servicios financieros y tengo un MBA de la London Business School. Su experiencia abarca todo el ciclo de vida del producto, desde las hojas de ruta estrat\u00e9gicas de alto nivel hasta la gesti\u00f3n diaria de equipos de entrega multidisciplinares.<\/p>\n\n\n\n<p>Tambi\u00e9n apoya el programa Data for Good de Snowflake y le apasiona utilizar la tecnolog\u00eda para resolver los retos del mundo. Cuando no esta evangelizando sobre la IA, le gusta disfrutar del arte experimental, hacer senderismo por la monta\u00f1a y asistir a conferencias sobre f\u00edsica cu\u00e1ntica.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hace una semana se celebr\u00f3 Codemotion Madrid 2025, los d\u00edas 20 y 21 de mayo en el espacio MEEU Madrid (Estaci\u00f3n de Chamart\u00edn). Fue un evento fant\u00e1stico, repleto de las \u00faltimas innovaciones tecnol\u00f3gicas. Una de las charlas m\u00e1s destacadas fue Building End-to-End ML Workflows in Python: From Data to Production, en la que se mostr\u00f3&#8230; <a class=\"more-link\" href=\"https:\/\/www.codemotion.com\/magazine\/es\/backend-es\/machine-learnig\/\">Read more<\/a><\/p>\n","protected":false},"author":64,"featured_media":29772,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[10606],"tags":[10747],"collections":[],"class_list":{"0":"post-33210","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-backend-es","8":"tag-desarrollo-web","9":"entry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v26.9) - 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