Model accuracy plateaued at 78% on image classification task

Improved accuracy to 92% using novel data augmentation and architecture tweaks

Latency exceeded 200ms per inference

Reduced latency to 45ms through model pruning and quantization

High false‑positive rate in fraud detection

Cut false‑positives by 60% with calibrated confidence thresholds

Model failed to generalize to new data sources

Achieved robust cross‑domain performance with domain adaptation techniques

Training cost exceeded $10k per experiment

Saved 70% compute cost by implementing mixed‑precision training