LLMOps: Shipping AI Applications to Production
The complete operations playbook for LLM-powered products. Observability with Langfuse, prompt management, evaluation pipelines, security, scaling, incident response, and infrastructure as code.
About This Course
Shipping an LLM feature once is table stakes. Running it reliably across thousands of users — monitoring quality, iterating on prompts without fear, detecting regressions, and deploying model upgrades safely — is LLMOps. This course covers the full operational lifecycle of production LLM applications: observability with Langfuse and Prometheus, prompt registry and versioning, automated evaluation pipelines, prompt injection defense, rate limiting and API key security, scaling patterns, incident response, infrastructure as code with Terraform, A/B testing, cost management, and AI governance. The single capstone project integrates all major course tools into one deployable production platform — all 13 modules build toward that artifact. LLMOps competence means understanding how the pieces fit, not running commands in isolation. Expect to spend 4–8 hours on the capstone.
What You'll Learn
- Instrument LLM applications with Langfuse traces, scores, and dashboards
- Build a prompt registry with versioning, blue/green deployments, and rollback
- Design automated evaluation pipelines using LLM judges and rule-based checks
- Defend against prompt injection and implement API key rate limiting
- Scale LLM serving infrastructure and optimize for latency under load
- Run A/B tests and shadow deployments to validate model upgrades safely
- Provision and manage cloud infrastructure with Terraform
- Implement cost attribution, GPU optimization, and budget alerting
Who Is This For?
Shipping LLM features but lacking observability, evals, and operational discipline
Experienced developers learning to operate AI components reliably
Building the internal LLMOps platform for their organization
Prerequisites
- Building AI-Powered Applications with APIs
- Solid Python and basic API development experience required
- Linux command line familiarity helpful for infrastructure modules
- No Docker or Kubernetes experience required — infrastructure topics are introduced from scratch where they appear
- Best suited to engineers actively running or preparing to run LLM-powered products in production