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stmontgo123/README.md

Featured Project

A hands-on enterprise AI inference reliability lab comparing PyTorch, TensorFlow/Keras, and ONNX Runtime, with a production path toward NVIDIA NIM, Triton Inference Server, TensorRT, GPU telemetry, Kubernetes/Run:ai, and enterprise observability.

Focus areas: NVIDIA AI infrastructure · AI inference · Runtime reliability · Benchmarking · SRE · Python · Infrastructure automation · Observability

The lab includes:

  • PyTorch, TensorFlow/Keras, and ONNX Runtime inference workflows
  • Repeatable latency and throughput benchmarking
  • Prediction consistency checks across runtimes
  • Simulated traffic-spike and degraded-node scenarios
  • Model-version and rollback simulation
  • Synthetic infrastructure-failure prediction data
  • Streamlit executive dashboard
  • Architecture, runbook, decision-defense, executive talk track, LinkedIn, and presentation documentation
  • A production architecture path toward NVIDIA NIM, Triton, TensorRT, GPU telemetry, Kubernetes/Run:ai, and Prometheus/Grafana

Design principle: The best AI model is not automatically the best production AI service. Runtime choice, portability, observability, recovery behavior, and infrastructure architecture determine whether inference can meet enterprise reliability objectives.

Repository: NVIDIA AI Runtime Reliability Lab

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    Executive-friendly RAG demo showing LLM hallucination vs grounded answers, plus bad RAG data vs good RAG data.

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  6. nvidia-ai-runtime-reliability-lab nvidia-ai-runtime-reliability-lab Public

    Enterprise AI inference reliability lab comparing PyTorch, TensorFlow, and ONNX Runtime, with a production path to NVIDIA NIM, Triton, TensorRT, and GPU-scale AI infrastructure.

    Python