Setting up your environment
System architects can perform a number of optional tasks, such as configuring the default application context for models and other Prediction Studio records or selecting an internal database where Prediction Studio records are stored. Prediction Studio also allows you to enable outcome inferencing and configure the model transparency policy.
By default, transitions to Prediction Studio from Dev Studio are disabled, which means that all rules open in Dev Studio. You can configure the EnableDevStudioTransitions dynamic system setting to enable such transitions.
- Setting access to Prediction Studio
Use the Prediction Studio to create, update, and monitor machine learning models. To access the portal, add the pxPredictionStudio portal to your access group.
- Changing your workspace
For the complete and multidimensional development of your application, switch from one workspace to another to change the tools and features that are available in your work environment. For example, you can create resources such as job schedulers in Dev Studio, and then manage and monitor those resources in Admin Studio.
- Specifying a repository for Prediction Studio models
Enable creating and storing machine learning models in your application by specifying a resilient repository for model training and historical data.
- Specifying a database for Prediction Studio records
Specify an internal database to enable Prediction Studio to read and write data when building predictive models.
- Configuring the default rule context
You can configure the default application context for models and other resources that are related to model development.
- Enabling outcome inferencing
When enabled, the outcome inferencing feature allows you to support Prediction Studio projects with additional data analysis steps that help you to handle unknown behavior.
- Analyzing example projects and models in Prediction Studio
Prediction Studio contains examples of predictive analytics projects, classification models, and sentiment models that are pre-installed. These projects are intended to be simple starting points to understand the functionality for each model type. You can access the example projects from the Predictions navigation panel.
- Enabling model updates
Replace active models in predictions with other models, scorecards, or fields, and then deploy the candidate models to production by updating Prediction Studio settings with the work queue for data scientists, email account for sending notifications, and analytics repository.
- Clearing deleted models in Prediction Studio
Use this option for occasional housekeeping of machine learning models. Clear models that are obsolete and you do not need them anymore. After you delete a model in Prediction Studio, you can still restore the rule instances in Dev Studio that also retrieves the associated machine learning models. When you clear deleted models, you remove all the data that was associated with the deleted models and you cannot restore the models.