Deployment & LLMOps
Fictional sample profileSam covers what happens after the demo works: packaging, deployment, tracing, cost control and the operational habits that keep LLM services predictable.
How Sam reviews work
Sam reviews deployments by trying to break them — sending slow, malformed or oversized requests — and then asks learners to find the cause using their own traces and logs.
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Sam teaches the operational side of LLM applications. Learners containerize their services, manage configuration and secrets properly, and deploy behind APIs with sensible rate limits, timeouts and caching.
A large part of Sam's modules is observability: tracing requests with OpenTelemetry, recording token usage and cost, and building dashboards and alerts that help explain a problem rather than just report it. Sam also covers latency budgets, streaming and the trade-offs between model size, speed and cost.
Sam encourages learners to write a short runbook for every service they deploy, even a learning project, because it forces clear thinking about how the system fails.
This is demo content. It does not describe a real person, employer, qualification or track record.
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DeploymentGuide10 min read
Latency and cost in LLM systems are driven by a short list of levers: tokens in, tokens out, which model runs, and how often you call it. This guide walks through each lever and the trade-offs that come with pulling it.
By Sam Okafor · sample author
Request path
web client
streams tokens to the UI
api · FastAPI
auth, input limits, rate limits
orchestrator
prompt templates, routing, guardrails
model
retriever
tools
Cross-cutting
DeploymentField note9 min read
When an LLM feature misbehaves, the first question is always what exactly the model saw. These field notes cover what to record per call, how to structure spans across a pipeline, and how to keep sensitive data out of your telemetry.
By Sam Okafor · sample author
Build the foundations, projects, and habits needed to design more dependable AI applications.