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AI Systems in Production

Six weeks on operating AI systems after launch: tracing, cost control, model routing, incident response, and the deployment patterns that make failure survivable rather than public.

Working Professionals

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Duration: 6 weeks
Level: Advanced
Format: Live online
Investment: Request pricing
Next cohort: Dates announced soon

Who this is for

You have built an LLM or agent system
Comfortable with containers and CI

This is not for

Anyone who has not yet built something to operate

What you leave with

A deployed system with distributed tracing and cost dashboards
A failure taxonomy built from your own production traces
An incident runbook with rollback criteria

Tools and stack

OpenTelemetryLangfuseDockerFastAPIPostgreSQLGitHub Actions

Curriculum

Module 1 — Observability

Instrument the full loop with spans
Track tokens, latency and cost per request
Decide what to alert on

Module 2 — Failure taxonomy

Categorise real production failures
Find the signals that predict them
Prioritise by user impact

Module 3 — Cost control

Profile spend honestly
Cache and route between model tiers
Set hard budget limits

Module 4 — Deployment patterns

Run in shadow mode against live traffic
Ship behind a canary
Define rollback triggers in advance

Module 5 — Regression control

Run evals in CI
Version prompts like code
Prevent silent behaviour drift

Module 6 — Incident response

Write the runbook before the incident
Practise a rollback
Produce a postmortem that changes something

How this program handles evaluation and governance

This course is largely responsible engineering by another name. Shadow deployment makes failure free. Rollback criteria defined in advance stop judgement calls under pressure. A failure taxonomy turns anecdotes into evidence. And a postmortem that changes something is the difference between an organisation that learns and one that repeats.