Data Science Workspace tutorials
Adobe Experience Platform Data Science Workspace uses machine learning and artificial intelligence to unleash insights from your data. Integrated into Adobe Experience Platform, Data Science Workspace helps you make predictions using your content and data assets across Adobe solutions. Data scientists of all skill levels have sophisticated, easy-to-use tools that support rapid development, training, and tuning of machine learning recipes - all the benefits of AI technology, without the complexity. To learn more, begin by reading the Data Science Workspace overview .
Create and publish a machine learning model
Create machine learning models to derive insights from Experience Platform data using Data Science Workspace. Begin by reviewing the complete Data Science Workspace workflow, including preparing data, authoring a model, training and evaluating a model, and operationalizing the model. To get started, visit the create and publish a machine learning model walkthrough .
Enrich profiles and segments with machine learning insights
Data Science Workspace provides the tools and resources to create, evaluate, and utilize machine learning models to generate data predictions and insights. When machine learning insights are ingested into a Real-time Customer Profile-enabled dataset, that same data is also ingested as profile records which can then be segmented into subsets of related elements by using ADobe Experience Platform Segmentation Service. To learn more, follow the enrich Profile with machine learning insights tutorial .
Publish a model as a service
Data Science Workspace allows you to publish your trained and evaluated Model as a Service, enabling users within your IMS Organization to score data without the need for creating their own Models. This can be done using the Platform user interface or the Sensei Machine Learning API. To get started, follow the publish a model as a service API tutorial or the UI tutorial .
Schedule a Model
Data Science Workspace allows you to set up scheduled scoring and training runs on a machine learning Service. Automating the training and scoring process can help maintain and improve a Service's efficiency through time by keeping up with patterns within your data. To learn more, visit the tutorial for scheduling a model using the UI .