This has been a joint project of NVIDIA and AWS Community Builder in Machine Learning program.

Unwanted and unsolicited bulk digital communication (“spam”) is responsible for substantial direct and indirect economic damage every year.

Traditional ways to identify spam by detecting certain keywords, manually reviewing text records, or even running Natural Language Processing (“NLP”) pipelines are no longer sufficient.

This article describes the architecture of the state-of-the art Spam Detection Engine of C-Suite Labs consisting of multiple inter-dependent distinct classifiers delivering real-time, high-performance superior accuracy with minimum required manual review.

This article focuses on the DistilBERT spam content model.

Application of NVIDIA Triton Inference Server for this use case provides the inference throughput 2.4 times higher than TorchScript Inference Server with 52.9 times lower model latency.

Please read the rest of the article at https://jiripik.com/2022/06/30/nvidia-triton-spam-detection-engine-of-c-suite-labs/