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AdvancedPremium~12 hours12 lessons

LLMOps and AI Observability

Operate AI systems with tracing, evaluation pipelines, regression tests, cost monitoring, and production dashboards. Learn the engineering discipline behind reliable LLM products.

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What you'll learn

Prompt versioning and experiment tracking
Dataset versioning for evaluation
Evaluation pipelines: automated and human
LLM-as-judge patterns and calibration
Distributed tracing for LLM calls
Token cost monitoring and budgets
Latency monitoring and p95 targets
Tool-call and agent monitoring
Regression testing for LLM outputs
Canary releases for prompt changes
AI incident response playbooks
Production observability dashboards

Learning outcomes

1Instrument LLM applications with structured traces
2Build automated evaluation pipelines with LLM-as-judge
3Monitor cost and latency with alerting thresholds
4Run regression tests before prompt changes ship
5Write and execute AI incident response runbooks

Related lessons

LLMOps Basics

8 min

AI Evaluation and Guardrails

8 min

Fine-Tuning vs RAG

8 min

Browse all lessons

Prerequisites

Production RAG EngineerAgentic AI Engineer

Practice quizzes

Llmops quizEvaluation quiz

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