Optimal training uses a single front-loaded burst when concept durations are DMRL, and back-loading when they are IMRL; deployment schedules are treated as quasi-convex optimization problems.
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Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
Optimal training uses a single front-loaded burst when concept durations are DMRL, and back-loading when they are IMRL; deployment schedules are treated as quasi-convex optimization problems.