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Production AI Radar
Continuous model retraining
Automated retrain pipelines triggered by data drift or schedule.
AssessMLOps
- Why this ring
- Powerful when eval gates and rollback exist. Dangerous when teams retrain without understanding why performance shifted.
- Production risk if ignored
- Automated retrains amplify bad data incidents across all endpoints.
- EU AI Act relevance
- Requires documented retraining triggers and human oversight for high-risk use cases.
- Typical effort
- months
- High FinOps impact
Use cases
- Scheduled retrains
- Drift-triggered pipelines
Adoption steps
- Require eval gate before auto-promote
- Cap blast radius per model
- Human approval for high-risk
- Log every retrain trigger
Related tools
In your assessment
Retrain policy review + blast-radius assessment