Model Life Cycles
Accelerate Operationalization of Al Initiatives
Model Life Cycles
Accelerate Operationalization of Al Initiatives
A Model Life Cycle (MLC) defines the requirements and processes for operationalizing a model. It includes detailed process workflows with well-defined steps for operating, governing and maintaining the model throughout its post-development life cycle, until it is retired.
Accelerate the operationalization of models with a library of pre-defined Model Life Cycle processes
  • Use pre-defined processes for registrating, operationalizing, managing model risk, and monitoring your models across the enterprise
  • Customize the processes for a model’s life cycle for specific business and technical needs by automatically capturing metadata and information about the model’s journey throughout its life cycle
  • Unite data scientists, data engineers, developers, IT operations, model operations, risk managers and business unit leaders through clearly defined processes
Model Risk Management Screenshot
Model Life Cycle - Registration Processes
Registration Processes The pre-defined model life cycle registration processes can be used to:
  • Deploy a newly registered model into your QA runtime
  • Run the model through a series of tests
  • Trigger an automated security scan
  • Validate that all required documentation is in production model inventory
  • Seek appropriate approvals before it is deployed into production
Model Risk Management Processes
Establish business and regulatory rules that enforce specified controls and ensure auditability with predefined processes that address your governance and regulatory requirements.
  • Maintain the Production Model Inventory as models are updated
  • Validate that all required documentation, background test, source code and approvals are complete
  • Integrate with your model risk management systems
Model Life Cycle - Model Risk Management Processes
Model Life Cycle - Operationalization Processes
Operationalization Processes
Pre-defined operational processes can be used to automate the steps required for incident reporting and change management, including re-testing and approvals. You can easily build these operational processes into any model’s life cycle with processes that include:
  • Straight-through deployment of models
  • User tasks to review and approve changes to the models
  • Integration with IT task management and ticketing systems
  • Run and create snapshots of optional testing
Monitoring Processes
Automate the monitoring of model performance and remediation stepsto ensure models are performing optimally with pre-defined processes that can be used for:
  • Champion/challenger model comparison
  • Statistical performance monitoring
  • Data and concept drift monitoring
  • Daily Back-testing
  • Interpretability
  • Fairness
  • Model retraining and refresh
Model Life Cycle - Monitoring Processes

Accelerate the operationalization of your models.