Observability Summit North America 2026

N-ATLaS is a multilingual African-language LLM we took from research to production on Kubernetes. This talk shows the end-to-end path we used to make it reproducible, observable, and affordable: data + finetune pipelines (artifacts, seeds, checkpoints), Argo-orchestrated training on mixed GPU pools, and a serving stack with Triton + KServe tuned for real traffic. I’ll walk through SRE guardrails that mattered for N-ATLaS (SLOs, golden signals, error budgets), supply-chain hygiene (image signing, provenance, model versioning), and the levers that cut cost-per-token while improving latency and uptime under pre-emptions. We’ll cover autoscaling, caching, model rollout strategies, and incident playbooks plus what we’d change after thousands of downloads and weeks of live usage. Expect hard-learned patterns, YAML you can run, and a plain-English checklist you can lift into your own cluster; whether you’re serving English or a low-resource language model.