ML model optimization for fast, cheap production inference

I take machine learning models that already work and make them dramatically faster and lighter for production, without touching their accuracy. If your model is correct in the notebook but too slow, too hungry for memory, or too expensive to serve at scale, that is exactly the gap I close. I have spent years turning research prototypes into services that answer in milliseconds instead of seconds, and I bring the same discipline to every engagement: measure first, change second, prove the result with numbers you can trust.

My work covers the whole optimization path. I convert models to portable runtimes such as ONNX and TensorRT, apply quantization to int8 or fp16, prune redundant weights and heads, fuse and simplify the computation graph, and tune the deployment for your exact target, whether that is a CPU server, a data center GPU, or a constrained edge device. Every step is validated against your own test set so accuracy stays within the tolerance you define. I never hand back a faster model that quietly got worse, because I benchmark latency, throughput, and quality side by side before and after.

You receive a clear report: the original numbers, the optimized numbers, the exact changes, and a reproducible pipeline you can rerun on new model versions. Typical results are two to eight times lower latency and a matching drop in serving cost, with accuracy held steady. Tell me your framework, target hardware, and latency budget, and I will tell you honestly what is achievable before we start.

— Conversion to ONNX, TensorRT, and other runtimes
— Quantization (int8, fp16) and structured or unstructured pruning
— Graph optimization, operator fusion, and layout tuning
— Benchmarking on CPU, GPU, and edge with a reproducible report