Framework Guides ML Model Optimization with Constraint-Driven Approach.

Machine learning models are increasingly deployed across diverse environments, making optimization a critical engineering challenge. While numerous techniques like quantization and pruning exist, practitioners often lack a structured approach to selection. This new framework proposes viewing model optimization as a multi-objective engineering problem driven by specific constraints. The framework identifies five key constraint dimensions: data availability, latency, memory, accuracy tolerance, and retraining budgets. By mapping empirical gains from various optimization techniques to these operational constraints, it provides a principled methodology. The authors illustrate its practical application through prescriptive decision frameworks for four common industrial scenarios.