Retraining trigger criteria
Define the conditions that initiate a retraining review: drift alert thresholds, scheduled evaluation cycles, feedback volume accumulation, or external events like regulatory changes or market regime shifts.
Method
Five-step retraining governance framework
Every time a model is retrained or updated, you are shipping a new product. Treating it as a casual technical update introduces invisible risk — especially in regulated financial services contexts where model behavior is subject to audit.
Data preparation and validation
Establish requirements for the training data used in each retraining cycle: provenance documentation, quality review, bias audit, and sign-off before training begins.
Evaluation gate before deployment
Require the retrained model to pass the same evaluation criteria as the original model — plus any new criteria introduced since the last release. No retrained model deploys without documented evaluation results.
Approval workflow and version control
Define who must approve each model update before deployment, including model risk management, compliance, and business owner sign-offs. Every version is tagged, documented, and preserved for rollback.
Audit trail and change documentation
Maintain a complete record of every model version: training data used, evaluation results, approval history, deployment date, and any incidents linked to that version.
Outputs
Artifacts produced by the process
Retraining policy document
Formal policy governing when retraining is initiated, how it is conducted, and who approves it.
- Trigger criteria and initiation process
- Data requirements and quality standards
- Approval authority by model risk tier
Version control specification
Technical and process requirements for model versioning, storage, and rollback capability.
- Version tagging convention
- Model artifact storage requirements
- Rollback procedure and decision criteria
Approval workflow diagram
Visual map of the retraining approval process from trigger to deployment.
- Approval steps and required signatories
- SLA per approval stage
- Expedited process for urgent updates
Model changelog template
Standardized documentation format for recording all changes associated with each model version.
- Training data summary and provenance
- Performance comparison vs. prior version
- Approvers, dates, and deployment record
Engagement Cadence
How the process runs in practice
Typical timeline: 2-3 weeks (framework design); ongoing per cycle
- Week 1: retraining trigger criteria and data preparation standards
- Week 2: evaluation gate design and approval workflow mapping
- Week 3: version control specification, audit trail design, and policy documentation
Output: a retraining governance framework that keeps model updates traceable, compliant, and reversible — protecting the firm from invisible model risk.