Graphenus integra distintas librerías de machine learning para permitir a los equipos de data science desarrollar cualquier tipo de modelo, abstrayéndolos de la complejidad de gestión de datos distribuidos (infraestructura, configuración, procesamiento, etc).
Facilidad de uso
Soporte para múltiples lenguajes de programación: Java, Python, Scala, R.
Integración out-of-the-box con cualquier fuente de información soportada por Graphenus.
Elevado rendimiento y escalabilidad
Ejecución de los modelos y algoritmos de machine learning sobre Spark.
Disponibilidad de múltiples modelos optimizados.
Escalabilidad garantizada gracias a Graphenus y su arquitectura de contenedores.
Amplio ecosistema
Elevado volumen de algoritmos de ML disponible (clasificación, regresión, recomendación, clustering, etc).
Utilidades tipo workflow para la transformación de features, definción de pipelines, evaluación de modelos, persistencia, etc.
Modelos de redes neuronales, algoritmos genéticos, tensores, etc.
Graphenus cuenta con una sólida base sobre la que apoyar el desarrollo de modelos de machine learning
Sobre la base disponible de Spark ML se añaden nuevas librerías para dotar a Graphenus de capacidades diferenciales de ML:
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