Overview
Running LLMs in production requires operational maturity: distributed tracing of multi-agent calls, cost monitoring, latency optimization, CI/CD pipelines for model updates, and Kubernetes-based serving at scale. This module covers the full LLMOps stack — from local development to production Kubernetes deployments with proper observability.
Concept Flashcards
6 cards — click to flip and test recall
1 / 60/6 mastered
System Architecture
1 interactive diagram — drag nodes · scroll to zoom · click for details
LLM CI/CD extends standard DevOps with prompt regression tests, model evaluation, cost estimation, and container builds — gating on quality before deployment to EKS.
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Prompt Testing
LLM Evaluation
Cost Check
Build & Deploy
Key Concepts
6 concepts — click to expand
LLM calls are non-deterministic and complex (chains of prompts, tools, retrievals). Tracing captures the full execution tree: each LLM call, token count, latency, cost, input/output, and errors. LangSmith and Langfuse implement OpenTelemetry-compatible traces with LLM-specific metadata.
Tech Stack
LangSmithLangfuseLogfireWeights & BiasesAWS EKSDockerKubernetesGitHub ActionsCloudWatch
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