Model Life Cycles
Accelerate Operationalization of Al Initiatives
Model Life Cycles
Accelerate Operationalization of Al Initiatives

Model Life Cycles are the blueprint for ModelOps

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 model operationalization with pre-defined Model Life Cycles
  • Use provided steps and processes for model registration, operations, risk and monitoring
  • Customize with your business, risk and technical KPIs and controls
  • Scale use of MLCs with metadata that defines the model’s unique path and requirements
  • Unite data scientists, data engineers, 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
Ensure all approvals, validations and documents are complete before models are deployed to production environments or applications. Registration processes include:
  • Deploy registered models into your QA runtime
  • Run models through a series of tests
  • Trigger and automated security scan
  • Validate required documentation is in production model inventory
  • Route for appropriate approvals for production deployment
Model Risk Management Processes
Specify and enforce your regulatory and business controls. Track and log all steps and actions for reproducibility and auditability
  • Maintain comprehensive and updated production model inventory
  • Validate completion of all required steps, approvals, documentation, background tests, and more
  • Integrate with your model risk management systems
Model Life Cycle - Model Risk Management Processes
Model Life Cycle - Operationalization Processes
Operationalization Processes
Accelerate the operationalization of models with automated, repeatable processes that track and orchestrate deployment and change.
  • Straight through deployment models
  • Orchestrate and validate change management tasks
  • Run and create snapshots of model testing and changes
  • Integrate with IT service management systems
Monitoring Processes
Ensure models are performing optimally with automated performance monitoring.
  • 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.