From Rehearsal to Live Broadcast
why getting a language model working in a demo or notebook is a genuinely different challenge than running it reliably in production, night after night.
Running language models in production without losing control of them.
why getting a language model working in a demo or notebook is a genuinely different challenge than running it reliably in production, night after night.
the core metrics an LLM system needs monitored continuously in production, beyond just whether it's technically still running.
how early LLM-powered features were deployed and maintained before LLMOps existed as a distinct discipline, and why that approach stopped scaling.
why LLM systems need the same deliberate deployment pipeline discipline as any other production software, adapted for their specific quirks.
how to version and roll back LLM systems in production without ever causing a visible outage for the people relying on them.
the pre-deployment testing and validation an LLM system needs before it ever reaches real production traffic.
why latency and throughput monitoring for LLM systems needs to account for patterns traditional infrastructure monitoring wasn't built to handle.
handling model and API failures gracefully in production, so one dead connection doesn't take down the whole broadcast.
why genuinely critical LLM-dependent systems need real redundancy and failover planning, not just graceful handling of individual call failures.
why production monitoring needs to watch for gradual model drift, not just sudden failures, and how that connects to the maintenance burden covered elsewhere in this content library.
why LLM cost management needs continuous, granular tracking, not just periodic review of the monthly bill.
how routing requests across multiple models lets a production system balance cost, quality, and reliability, rather than committing to one model for everything.
why production LLM systems need a real content moderation and safety filtering layer, not just trust that the model will always behave.
why comprehensive observability and logging for LLM calls is essential for debugging, auditing, and understanding real production behavior.
how compliance and governance requirements shape production LLM systems, from data handling to auditability.
why practicing incident response before a real production failure happens matters as much as the technical safeguards themselves.
the deliberate process for upgrading or switching the underlying model in a production LLM system without disrupting what's already working.
how ongoing evaluation and user feedback loops keep a production LLM system improving over time, rather than just staying static after launch.
the sustained organizational discipline it takes to run an LLM system reliably over months and years, not just through a successful launch.
reassembling every station covered across this series into the complete picture of a well-run LLMOps practice, from deployment through sustained operation.