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

TL;DR

This paper develops a theoretical framework for optimal resource allocation in ML model training and deployment under concept drift, considering budget constraints, deployment timing, and communication limits, with practical algorithms for near-optimal performance.

Contribution

It introduces a model-agnostic framework linking resource allocation, concept drift, and deployment, deriving optimal policies for sudden concept changes and communication-constrained deployment.

Findings

Optimal training policies depend on concept aging properties.

Heuristics are suboptimal under certain drift distributions.

Proposed scheduling strategy achieves near-optimal client performance.

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.