Erste Schritte mit Data Science Workspace für Datenwissenschaftler
Erfahren Sie mehr über Data Science Workspace in Adobe Experience Platform. Diese Wiedergabeliste ist für Datenwissenschaftler konzipiert, die lernen möchten, wie JupyterLab Notebooks Einblicke und Abfragedaten ableiten, profilaktivierte Datensätze erstellen, automatisierte Modelle für maschinelles Lernen veröffentlichen und maschinengelernte Einblicke für Adobe- und Nicht-Adobe-Anwendungen aktivieren kann.
Übersicht über Data Science Workspace
Die Vision des maschinellen Lernens in Adobe Experience Platform besteht darin, die Datenwissenschaft zu demokratisieren, indem das domänenbezogene Know-how von Adobe-Produkten, Kunden und Partnern genutzt wird, um ein Ökosystem intelligenter Dienste zu schaffen, das die nächste Generation von Kundenerlebnissen ermöglicht. Data Science Workspace erleichtert den Zugriff auf kanalübergreifende Daten, das Erstellen von Modellen, die Implementierung von Modellen mit einer 1-Klick-Implementierung und die Nutzung von Modelleinblicken durch die Freigabe über Echtzeit-Kundenprofile. In diesem Video erhalten Sie einen Überblick darüber, was Data Science Workspace ist und welche Vorteile es für Unternehmen bietet.
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Übersicht über die Architektur von Data Science Workspace
In diesem Video werden die übergreifende Architektur und die Hauptkomponenten von Data Science Workspace in Adobe Experience Platform beschrieben.
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Kursschema und -datensatz erstellen
Erfahren Sie, wie Sie den Datensatz und das Schema des Data Science Workspace-Kurses erstellen, die im Rest des Kurses verwendet werden.
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Daten in JupyterLab-Notebooks laden
In diesem Video erfahren Sie, wie Sie ein JupyterLab-Notebook erstellen und Daten aus Adobe Experience Platform laden. Außerdem wird gezeigt, wie Sie die Leistung Ihres Notebooks bei der Arbeit mit großen Datenmengen steigern können.
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Abfragen und Entdecken von Daten in Data Science Workspace
Mit Adobe Experience Platform können Sie SQL (Structured Query Language) in Data Science Workspace verwenden, indem Sie Query Service als Standardfunktion in JupyterLab integrieren.
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Explorative Datenanalyse in Data Science Workspace
Das Tutorial zur Analyse von Explorationsdaten (EDA) soll Ihnen dabei helfen, Muster in Daten zu ermitteln, die Datensicherheit zu überprüfen und die relevanten Daten für Vorhersagemodelle zusammenzufassen.
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Rezepte, Modelle und Dienste - Übersicht
Erfahren Sie mehr über Rezepte, Modelle und Dienste in Adobe Experience Platform Data Science Workspace.
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Leistung des Modells analysieren
Erfahren Sie mehr über verschiedene Methoden zur Analyse der Leistung eines Modells, z. B. eine Verwirrungsmatrix, Genauigkeit, Rückruf und Präzision.
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Erstellen eines Modells mithilfe der Rezept-Builder-Vorlage
In diesem Video wird die Verwendung der Vorlage "Rezept-Builder"im JupyterLab-Starter gezeigt, um ein Tendenzmodell zu trainieren und zu bewerten und ein Rezept zu erstellen.
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Erstellen und Veröffentlichen eines trainierten Modells
Erfahren Sie, wie Sie ein Modell mithilfe eines mit dem JupyterLab-Rezept-Builder-Notebook erstellten Rezepts erstellen, trainieren, bewerten und veröffentlichen.
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Planen automatisierter Schulungen und Auswertungen für einen Dienst
Erfahren Sie, wie Sie in Data Science Workspace eine automatisierte Schulung und Auswertung für einen Dienst einrichten.
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maschinelle Lernausgabe in Segmentierung verwenden
Erfahren Sie, wie die Data Science Workspace-Modellausgaben im Echtzeit-Kundenprofil und in der Segmentierung verwendet werden können.
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