Get Started with Data Science Workspace for Data Scientists
Learn about Data Science Workspace in Adobe Experience Platform. This playlist is designed for data scientists who want to learn how to use JupyterLab Notebooks to derive insights and query data, create profile-enabled datasets, publish automated machine learning models, and activate machine-learned insights to both Adobe and non-Adobe applications.
Data Science Workspace overview
The vision of machine learning on Adobe Experience Platform is to democratize data science by using the domain expertise of Adobe products, customers, and partners to create an ecosystem of intelligent services to power the next generation of customer experiences. Data Science Workspace makes it easy to access omni-channel data, build models, operationalize models with a one-click deployment, and consume model insights by sharing them via real-time customer profiles. This video gives an overview of what Data Science Workspace is and the value it provides to businesses.
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Data Science Workspace architecture overview
This video describes the overarching architecture and illustrates the primary components of Data Science Workspace in Adobe Experience Platform.
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Create the course schema and dataset
Learn how to create the Data Science Workspace course dataset and schema that are used in the remainder of the course.
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Load data in JupyterLab notebooks
This video shows how to create a JupyterLab notebook and load data from Adobe Experience Platform. It also shows how you can increase the performance of your notebook when working with large amounts of data.
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Query and discover data in Data Science Workspace
Adobe Experience Platform allows you to use Structured Query Language (SQL) in Data Science Workspace by integrating Query Service into JupyterLab as a standard feature.
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Exploratory Data Analysis in Data Science Workspace
The Exploratory Data Analysis (EDA) tutorial is designed to assist you with discovering patterns in data, checking data sanity, and summarizing the relevant data for predictive models.
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Recipes, models, and services overview
Learn about recipes, models, and services in Adobe Experience Platform Data Science Workspace.
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Analyze model performance
Learn about some of the different methods used to analyze the performance of a model such as a confusion matrix, Accuracy, Recall, and Precision.
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Build a model using the recipe builder template
This video showcases using the recipe builder template in the JupyterLab launcher to train and score a propensity model and create a recipe.
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Create and publish a trained model
Learn how to create, train, evaluate, and publish a model using a recipe made with the JupyterLab recipe builder notebook.
184
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Schedule automated training and scoring for a service
Learn how to set up automated training and scoring for a service in Data Science Workspace.
172
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Use machine learning output in segmentation
Learn how Data Science Workspace model outputs can be used in Real-Time Customer Profile and segmentation.
387
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