Operating at Scale
Autoscaling, observability, safe rollouts, reliability patterns and a monitored deployment lab.
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- ▶ Up next14 min+50 XPAutoscaling LLM workloadsWhy CPU-based autoscaling fails for GPUs, which signals work (queue depth, in-flight requests), how to set targets…
- 14 min+50 XPObservability for LLM servicesThe metrics, logs and traces that matter for LLM serving: user-facing and capacity metrics, histograms and…
- Pro14 min+50 XPRollouts and model versioningTreat models like releases: immutable bundles, quality gates, shadow traffic, canaries with real sample-size maths…
- Pro14 min+50 XPReliability patterns: limits, backpressure and load sheddingKeep the service up when demand exceeds capacity: bounded queues sized with Little's law, admission control…
- LabPro60 min+100 XPLab: ship a monitored deploymentStand up two model-server replicas behind an nginx gateway with Prometheus and Grafana, load-test it, find the…