arXiv:2603.09024v2 Announce Type: replace Abstract: Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPER - a detector- and model-agnostic, data-only test that estimates the post-drift data size required for stable retraining. CALIPER exploits state dependence in streams generated by dynamical systems: we run a single-pass weighted local regression over the post-drift window and track a one-step proxy error as a function of a locality parameter $\theta$. When an effec
The increasing deployment of AI systems in dynamic real-world environments necessitates robust methods for managing concept drift and ensuring model reliability.
This development offers a pragmatic, data-only solution for critical AI challenges, directly impacting the operational stability and cost-efficiency of deployed AI models.
The ability to accurately determine when and how much data is needed for retraining after concept drift provides a standardized, model-agnostic approach to maintaining AI performance.
- · Companies deploying AI in dynamic environments
- · MLOps platforms and tooling
- · AI researchers focused on robustness
- · Organizations with brittle, manually managed AI deployments
- · Systems that lack automated drift detection
AI systems will become more resilient and adaptive to changing real-world conditions with less manual intervention.
Improved model stability will accelerate the adoption of AI in high-stakes applications where reliability is paramount.
The reduced overhead for AI model maintenance could lower operational costs and free up resources for further AI innovation and deployment.
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