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

This article likely discusses strategies for efficiently managing computational resources when training and deploying machine learning models, particularly focusing on the challenges posed by concept drift (changes in the data distribution over time). The research probably explores methods to dynamically adjust resource allocation to maintain model performance and minimize costs.