We bridge the gap between experimental algorithms and enterprise production. We engineer high-throughput model inference endpoints, automated continuous retraining pipelines, and drift-monitoring systems with verifiable SLA guarantees.
Deterministic, Observable Model Lifecycles
Optimized microservice runtimes utilizing ONNX, TensorRT, and asynchronous batching to serve sub-10ms predictions at 20k+ concurrent QPS.
Automated Kolmogorov-Smirnov and population stability tests that trigger quarantine alerts and retraining pipelines when incoming production distributions change.
Centralized, version-controlled feature repositories ensuring training-serving skew is completely eliminated across real-time and batch workloads.