About This Course
Most AI systems work in a demo and quietly break in production. LLMOps & AI Reliability is the specialization that prevents that. It teaches the operational discipline that production AI teams depend on — and that almost no course teaches at this level.
You will master AI observability (tracing LLM and agent systems with OpenTelemetry, the 2026 standard), the modern tooling stack (LangSmith, Langfuse, Helicone, Braintrust, Promptfoo), and how to design evaluation sets at scale — both curated offline sets and online LLM-as-judge evals running on real production traces. You'll track the metrics that matter (task success, latency, cost-per-task), build regression pipelines that re-run on every change, and detect the drift that silently degrades AI systems over time.
The centerpiece is the "ship gate": a versioned eval set, a numerical score, and a regression alarm — the discipline that separates teams who ship reliably from teams who "ship by vibe" and regress within sixty days. You'll build eval-gated CI/CD that blocks bad changes before they reach users, and learn incident response for when an AI system misbehaves in production.
Through hands-on projects and a comprehensive capstone building a complete LLMOps pipeline around a real AI application, you'll graduate ready for LLMOps Engineer, Eval Engineer, and AI Reliability Engineer roles — premium specializations in high demand. Our Human Intelligence approach ensures you develop the judgment to decide what to measure and what "good enough to ship" really means.