Home GPU ML Lab (Independent Project) — 2024–Present
Other
Built and ran an independent home GPU ML lab to support end-to-end model training and iterative evaluation. Documented experiment settings, training parameters, and results to enable reproducibility for model assessment. Performed analysis and improvements aligned with quantization-driven deployment efficiency on limited hardware. • Prepared training/evaluation inputs and managed training loops in PyTorch. • Applied model quantization to reduce inference footprint and validate efficiency gains. • Recorded experiment configurations and outcomes for repeatable benchmarking. • Used ML familiarity to support downstream AI training/evaluation workflows.
2024 - Present