What is multi-objective optimization in ML model deployment?

Multi-objective optimization balances competing goals like accuracy, latency, cost, and fairness.

In production ML systems, optimizing only for accuracy is insufficient. Multi-objective optimization considers trade-offs between performance metrics such as latency, cost, energy consumption, and fairness constraints. Techniques include Pareto optimization, weighted loss functions, and reinforcement learning-based tuning. This is critical in large-scale systems where infrastructure cost and user experience must be balanced.