Introducción a Data Science Workspace para científicos de datos
Obtenga información sobre Data Science Workspace en Adobe Experience Platform. Esta lista de reproducción está diseñada para científicos de datos que deseen aprender a utilizar JupyterLab Notebooks para obtener perspectivas y datos de consulta, crear conjuntos de datos con perfil habilitado, publicar modelos automatizados de aprendizaje automático y activar perspectivas aprendidas por el equipo para aplicaciones de Adobe y que no sean de Adobe.
Información general del espacio de trabajo de ciencia de datos
El objetivo del aprendizaje automático en Adobe Experience Platform es democratizar la ciencia de datos mediante el uso de la experiencia de dominio de productos de Adobe, clientes y socios para crear un ecosistema de servicios inteligentes para impulsar la próxima generación de experiencias del cliente. Data Science Workspace facilita el acceso a datos omnicanal, la creación de modelos, la puesta en funcionamiento de modelos con una implementación en un solo clic y el consumo de perspectivas de modelos compartiéndolas a través de perfiles de clientes en tiempo real. Este vídeo ofrece información general sobre qué es Data Science Workspace y el valor que proporciona a las empresas.
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Resumen de arquitectura de Data Science Workspace
En este vídeo se describe la arquitectura general y se ilustran los componentes principales de Data Science Workspace en Adobe Experience Platform.
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Creación del esquema y el conjunto de datos del curso
Aprenda a crear el conjunto de datos y el esquema del curso Data Science Workspace que se utilizan en el resto del curso.
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Este vídeo muestra cómo crear un portátil JupyterLab y cargar datos de Adobe Experience Platform. También muestra cómo puede aumentar el rendimiento de su portátil cuando trabaja con grandes cantidades de datos.
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Consulta y detección de datos en Data Science Workspace
Adobe Experience Platform le permite utilizar el Lenguaje de consulta estructurado (SQL) en Data Science Workspace integrando el Servicio de consulta en JupyterLab como función estándar.
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Análisis de datos exploratorios en Data Science Workspace
El tutorial de análisis exploratorio de datos (EDA) está diseñado para ayudarle a descubrir patrones en los datos, comprobar la sanidad de los datos y resumir los datos relevantes para modelos predictivos.
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Información general sobre fórmulas, modelos y servicios
Obtenga información sobre fórmulas, modelos y servicios en Adobe Experience Platform Data Science Workspace.
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Analizar el rendimiento del modelo
Obtenga información acerca de algunos de los diferentes métodos utilizados para analizar el rendimiento de un modelo, como una matriz de confusión, Precisión, Recuperación y Precisión.
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Crear un modelo con la plantilla del generador de fórmulas
Este vídeo muestra el uso de la plantilla del creador de fórmulas en el lanzador de JupyterLab para entrenar y puntuar un modelo de tendencia y crear una fórmula.
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Creación y publicación de un modelo entrenado
Aprenda a crear, entrenar, evaluar y publicar un modelo con una fórmula hecha con el bloc de notas del generador de fórmulas de JupyterLab.
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Programar formación y puntuación automatizadas para un servicio
Aprenda a configurar la formación y la puntuación automatizadas para un servicio en Data Science Workspace.
172
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Usar la salida de aprendizaje automático en la segmentación
Descubra cómo se pueden utilizar los resultados del modelo de Workspace de ciencia de datos en la segmentación y el perfil del cliente en tiempo real.
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