Systems That Last.

An intro to NVIDIA Triton Inference Server, why decoupling model inference from the serving flow solves a real resource and versioning problem, and where Triton stops making sense.

Part 2 of cross-platform model bundling. How to prove that a model trained in Python produces identical scores when served from Java: golden records, shadow scoring, statistical tolerance bands, and case studies from Uber, LinkedIn, Airbnb, DoorDash, and Netflix.

A deep look at cross-platform model bundling formats: MLeap, ONNX, PMML, MLflow, TorchScript, and BentoML. Not a format survey but a framework for choosing, with real-world examples from Meta, Microsoft, Hugging Face, and Databricks.