Data Cloud App Template Configuration for Predictions

Create and set up a template configuration to build customizable Data Cloud apps that generate predictions for your scoring use case.

• Create a Data Cloud Template Configuration
  Create a Data Cloud template configuration to generate prediction scores for your use case. The template configuration creates a Data Cloud app, an Einstein Discovery model, and preconfigured recipes based on the settings defined in the template configuration.
• Select a Dataspace
  Select the dataspace containing the required data model object for setting up your scoring model.
• Select a Data Model Object for Training and Scoring
  Select the data model object with data to train your model and get predictions.
• Define the Target for the Prediction
  Define the variable that you want to use as your model’s primary focus for analysis and predictions. Select conditions based on the fields from the object or the data model object with additional features. The scoring model uncovers relationships between the input features and your target variable, and provides insights into maximizing or minimizing your target variable.
• Select Input Features to Get Accurate Predictions
  An input feature is a field value that can influence prediction. Your features can be account fields, CRM Analytics predefined fields, or fields from the Data Cloud data model object that you’ve selected for training and scoring.
• Save Assets to Debug Data Cloud App Installation Failures
  Save your Data Cloud app assets within the app to maintain access to the assets even if the app installation fails. Use the saved assets to easily debug app installation failures.
• Filter Your Training and Scoring Datasets
  Focus your predictions on a specific subset of the object data by defining filter conditions. Get more useful predictions by excluding the records that are irrelevant to your prediction. Make sure that you have sufficient records in your training and scoring datasets by counting the records. Filtering is optional. You can use the complete dataset for training and scoring.
• Store Predictions in Records
  View your generated predictions contextually by storing them in records. To write back a prediction, select a preconfigured output connector and then select an object and field to store the prediction in. You can write back predictions only if you’ve trained and deployed the model for a template configuration.
• Show Real-Time Predictions and Next Best Action Recommendations by Using AI Accelerator
  Show prediction scores, suggestions and insights about prediction scores, and Next Best Action recommendations for your batch and real-time use cases by creating an AI Accelerator use case. Customize the configuration of the AI Accelerator use case by defining your own feature extraction settings and Einstein Discovery model. You can create an AI Accelerator use case only if you’ve trained and deployed the model for a template configuration.