Ottimizzazione e Compressione Modelli

Systematic optimization of trained models to improve inference speed, reduce resource requirements, and lower operational costs while maintaining accuracy. We employ techniques including quantization, pruning, knowledge distillation, and architecture search to create efficient models suitable for production deployment. Our optimization considers target deployment environments whether cloud servers, edge devices, or mobile platforms. We benchmark optimized models against originals to ensure acceptable accuracy-efficiency trade-offs. This is crucial for real-time applications, high-volume services, or resource-constrained environments. The optimization can reduce model size by 10x or more and inference time by several orders of magnitude, enabling applications that wouldn't be feasible with full-scale models.