Use the Prediction Studio to create, update, and monitor machine learning models. To access the portal, add the pxPredictionStudio portal to your access group.

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.

Enable creating and storing machine learning models in your application by specifying a resilient repository for model training and historical data.

Specify an internal database to enable Prediction Studio to read and write data when building predictive models.

You can configure the default application context for models and other resources that are related to model development.

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.

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.

Enable Machine Learning Operations (MLOps) so that you can replace active models in predictions with other models, scorecards, or fields, and then deploy the candidate models to production. To enable MLOps, update Prediction Studio settings with the work queue for data scientists, the analytics repository, and optionally, an email account for sending notifications.

Monitor input, that is, predictors, and output of your models and predictions to observe whether they behave as you expect.

Define when you want to receive Prediction Studio notifications about changes in the performance of your predictions and models. Adjust the notification settings to control which kind of changes trigger notifications.

Data scientists that work in Prediction Studio can get a daily email with high-severity notifications from the last 24 hours. The daily emails alert the data scientists to important issues that affect models and predictions in your system, such as significant drops in performance.

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.