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Model Life Cycle Engine

Automated Process Workflows

Model Life Cycles are the blueprint for ModelOps

A Model Life Cycle (MLC) automates 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 to automate 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

Automated 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

Automated 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

Automated 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

Reflexive 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.