About the service
I take working machine learning models and make them significantly faster and lighter for production without sacrificing accuracy. If a model predicts correctly in a notebook but is too slow, too memory-hungry, or too expensive to serve under load, that's exactly the problem I solve. Over the years I've turned dozens of research prototypes into services that respond in milliseconds instead of seconds, and in every project I follow one principle: measure first, then change, then prove the results with numbers you can trust.
I own the full optimization path. I convert models to portable runtimes like ONNX and TensorRT, apply int8 or fp16 quantization, remove excess weights and heads with pruning, fuse and simplify the compute graph, tune deployment for your specific hardware — whether CPU server, data-center GPU, or edge device. Each step is validated on your test set, so accuracy stays within your tolerance. I never hand off a faster model that silently got worse: I measure latency, throughput, and quality side-by-side before and after.
You get a clear report: baseline numbers, post-optimization numbers, exact list of changes, and a reproducible pipeline you can rerun on new model versions. Typical result — two-to-eight times latency reduction and similar cost savings with preserved accuracy. Name the framework, target hardware, and latency budget, and I'll honestly say what's 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
— Benchmarks on CPU, GPU, and edge with reproducible reports
I own the full optimization path. I convert models to portable runtimes like ONNX and TensorRT, apply int8 or fp16 quantization, remove excess weights and heads with pruning, fuse and simplify the compute graph, tune deployment for your specific hardware — whether CPU server, data-center GPU, or edge device. Each step is validated on your test set, so accuracy stays within your tolerance. I never hand off a faster model that silently got worse: I measure latency, throughput, and quality side-by-side before and after.
You get a clear report: baseline numbers, post-optimization numbers, exact list of changes, and a reproducible pipeline you can rerun on new model versions. Typical result — two-to-eight times latency reduction and similar cost savings with preserved accuracy. Name the framework, target hardware, and latency budget, and I'll honestly say what's 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
— Benchmarks on CPU, GPU, and edge with reproducible reports
