.tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="ed3832aa6201cbec5b1e5b902dc067e2"] { padding: 25px; } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="d4e56170aac41dbd8cced71fe18cf47d"] { padding: 25px;margin-top: 5vh; } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="148003e0f263c2018a15f05b25afb459"] { background: url('https://www.modus.es/wp-content/uploads/2021/03/Rapidminer-Imagen-contenido-primero-oscura-media.jpg') center center no-repeat;background-size:cover;padding: 50px 25px 50px 25px; } .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="148003e0f263c2018a15f05b25afb459"] > .tb-container-inner { max-width: 1200px; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="fa51a3888525655adff0fcd7cd1f81dd"] { display: flex; } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="fa51a3888525655adff0fcd7cd1f81dd"] p { line-height: 1.2em; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] { margin-top: 5vh;margin-bottom: 5vh;grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="84d852bd56e31223f5b4478885a9f112"] { padding: 25px;min-height: 500px;display:ms-flexbox !important;display:flex !important;-ms-flex-direction:column;flex-direction:column;-ms-flex-pack:center;justify-content:center; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] { margin-right: 100px;margin-left: 100px;grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="e863d66eac949908a5c6c8d2fd31320c"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="83e03655446882dc27199d5f59017e0c"] { padding: 50px 25px 25px 25px;min-height: 271px;display:ms-flexbox !important;display:flex !important;-ms-flex-direction:column;flex-direction:column;-ms-flex-pack:center;justify-content:center; } @media only screen and (max-width: 781px) { .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="fa51a3888525655adff0fcd7cd1f81dd"] { display: flex; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="e863d66eac949908a5c6c8d2fd31320c"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto} } @media only screen and (max-width: 599px) { .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"] { grid-template-columns: minmax(0, 1fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="3d9c399fe625e1b972f817a0680229f6"]  > .tb-grid-column:nth-of-type(1n+1) { grid-column: 1 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="fa51a3888525655adff0fcd7cd1f81dd"] { display: flex; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"] { grid-template-columns: minmax(0, 1fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="2a96f1b5762b46d65db8abd60178279e"]  > .tb-grid-column:nth-of-type(1n+1) { grid-column: 1 } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"] { grid-template-columns: minmax(0, 1fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fdc3d89fb85b887a47d4a71d608ebc67"]  > .tb-grid-column:nth-of-type(1n+1) { grid-column: 1 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="e863d66eac949908a5c6c8d2fd31320c"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto} } 
RapidMiner: Complejos Modelos para Data Scientists y al mismo tiempo facilidad de uso para Usuario de Negocio
¿Por qué cuando hablamos de Predicción Analítica y Machine Learning mencionamos RapidMiner?
RapidMiner cuenta con:
Auto-modelos de Machine Learning con casos de uso; Haga prototipos de Machine Learning de una forma rápida, oriente su compañía hacia políticas de ahorro, aumente sus ingresos y gestione el riesgos.
Entorno colaborativo para todo tipo de usuario; Tanto usuarios de negocio, usuarios más avanzados y desarrolladores de código en Python o R, tienen nun entorno colaborativo para gestionar de principio a fin un proyecto de Machine Learning.
Preparación de datos universal e intuitiva; Promueva la gestión corporativa a través de los datos y el análisis, gracias a la fácil administración y re-utilización de los costosos procesos de preparación de datos, creando workflows transparentes para los usuarios.
Avanzado para DataScientists, Simple para todo el resto del mundo
Otras Plataformas
Lentitud para predecir oportunidades y detectar riesgos
Alta especialización y gran expertise
Modelos Opacos
Herramientas no integradas en el desarrollo de un proyecto completo
Soluciones cerradas
Con RapidMiner
Búsqueda rápidas de revulsivos e impactos de negocio
Avanzado para data scientists, simple y amigable para todos los demás
Total transparencia, fácil gobernanza de Machine Learning
Plataforma colaborativa de principio a fin (End-to-End) para el desarrollo de un proyecto
Solución Open source y extensible
Si deseas más información llámanos al 91 710 62 77 o envíanos un email a modus@modus.es
