AI/ML model optimization techniques

Oct 2025

Experimenting with pruning, post-training quantization, and CNN deployment benchmarks.

Key Insights & Takeaways

•Deep learning deployment heuristics: Reducing float32 weights to int8 through post-training quantization with TensorFlow Lite.
•Benchmarking: Comparing magnitude-based weight pruning against structured layer dropping on custom vision datasets. Achieved a 3.4x model footprint reduction with under 0.8% loss in top-1 validation accuracy.
•Production Takeaway: Always profile inference latency on target hardware (edge device vs CPU vs cloud GPU) before optimizing architecture.