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
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
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.
