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
