Core

SLOs for AI Systems

SLOs define the service outcomes users or training teams need. Choose measurable latency, availability and progress targets, then use their error budgets to guide decisions.

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RELATED CONCEPTS

Latency Metrics: TTFT, TPOT and Goodput Training Uptime and Interruption Statistics Designing for Latency SLOs Capacity and Backpressure Incident Response for GPU Fleets Inference Autoscaling and Cold Starts

LESSONS THAT TEACH THIS

Foundations of AI InfrastructureConnect request delays to scheduler and device evidence19 min→Inference EngineeringCapstone: investigate an SLO miss and write a usable runbook330 min→

PRACTICE THIS IN REAL QUESTIONS

GPU Fleet Reliability & ObservabilityDefine the service level objectives for an LLM serving fleet, and the alerting that tells you when one is about to be missed.→LLM Inference & ServingDefine TTFT, TPOT and goodput, and tell me how you would measure each one in production.→GPU Fleet Reliability & ObservabilityWhat service level objectives would you promise researchers on a training platform, and how would you measure each?→Open-Weights Models & Serving EnginesDesign the benchmark you would run before putting a new open-weights model into production.→LLM Inference & ServingWhy do prefill and decode behave so differently, and why does that matter for the hardware you serve on?→LLM Inference & ServingA long prompt arrives while sixty users are mid-generation. What happens, and how does chunked prefill fix it?→