Simplify the tedious and often error-prone steps of creating, managing, validating, administering and monitoring your analytic models. Govern models as managed assets. Centralize activities. And adapt quickly by refining models when performance degrades.
Confirm model validity and track usage.
With an efficient, repeatable process for registering, validating, monitoring and retraining models, you can trace analytical models from their inception to their usage and ultimate retirement. Accountability metrics and version control status reports record who changes what and when – with full versioning, history and lineage relationships.
Monitor performance and stay current.
An iterative framework lets you test and compare your analytical models, generate necessary benchmark reports and – when models are deployed – push performance metrics to established reporting channels. Modelers can easily collaborate and reuse models. And you can set automatic alerts, detecting performance degradation for retraining and refinement.
Safeguard audit trails and compliance.
Compliance demands full visibility into model usage and application. The centralized model repository and comprehensive inventory track model life cycle and provide version control. Analytical processes are fully traceable, which eases audit and compliance tasks for internal governance and external regulations. Basel II risk model validation reports provide total transparency into the soundness of internal credit risk measurement systems and enable fast answers to on-demand regulatory inquiries.
Streamline management workflow.
This web-based software streamlines the manual, error-prone steps of creating, managing, administering and monitoring the deployment of analytical models. An automated and collaborative model management process lets you create customized workflows for different types of models and users. From data scientists to data miners, all experience a consistent view of model currency, historic versions and performance that empowers them to take relevant action.
- Centralized repository. Easily manage analytical models via a centralized, secure web-based repository.
- Analytical workflow management. Define and track custom workflows for model lifecycle management using a web-based interface.
- Model validation. Validate model scoring logic before models are defined to production systems.
- Performance monitoring and reporting. Monitor and report model performance during test and production life cycles.
- Overall model lifecycle management. Manage projects collaboratively with prebuilt model lifecycle templates.
Learn how you can operationalize analytics to derive business value.