Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift

• URL: http://arxiv.org/abs/2512.12816v1
• Date: Sun, 14 Dec 2025 19:42:04 GMT
• Title: Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
• Authors: Hasan Burhan Beytur, Gustavo de Veciana, Haris Vikalo, Kevin S Chan,
• Abstract summary: We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets.<n>We show that training policies depend critically on aging concept properties, and that intuitive durations are provably suboptimal under Increasing Residual Life (IMRL)<n>These results offer theoretical and foundations for algorithmic ML model management under concept, with implications for continual learning, distributed, and adaptive ML systems.
• Score: 22.3333396225412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provider distributes trained models to multiple clients whose devices support local inference but lack the ability to retrain those models, placing the burden of performance maintenance on the provider. We introduce a model-agnostic framework that captures the interaction between resource allocation, concept drift dynamics, and deployment timing. We show that optimal training policies depend critically on the aging properties of concept durations. Under sudden concept changes, we derive optimal training policies subject to budget constraints when concept durations follow distributions with Decreasing Mean Residual Life (DMRL), and show that intuitive heuristics are provably suboptimal under Increasing Mean Residual Life (IMRL). We further study model deployment under communication constraints, prove that the associated optimization problem is quasi-convex under mild conditions, and propose a randomized scheduling strategy that achieves near-optimal client-side performance. These results offer theoretical and algorithmic foundations for cost-efficient ML model management under concept drift, with implications for continual learning, distributed inference, and adaptive ML systems.

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