Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

When tracing, evaluation and gateway tools are set up in isolation, engineering teams still end up relying on manual spot-checks and trial-and-error in production. This comprehensive programme focuses on wiring every LLMOps layer around an enterprise RAG or agent application into a single, connected engineering pipeline.

In hands-on labs participants integrate model gateways, versioned prompt registries, PR-triggered eval and regression suites, runtime guardrails and live trace feedback loops, leaving with a working reference LLMOps template and trace dashboard design.

Who Should Attend?

  • AI and software engineers operating LLM, RAG or agent systems in production
  • MLOps and platform teams establishing a company-wide LLMOps standard
  • Technical leads and architects accountable for engineering quality in AI projects
  • Prerequisite: Python, Docker, Git workflows and prior LLM application development experience

Key Takeaways

  • Unify model gateways, prompt registries, tracing and evaluation tools in one architecture
  • Build automated regression and quality gates that run on every prompt or model change
  • Capture failing production traces and convert them into golden eval test cases
  • Consolidate cost, latency, safety and quality metrics into an operational dashboard
  • Create a reusable enterprise LLMOps project template for multiple teams

Curriculum

End-to-End LLMOps Reference Architecture
  • Connecting development, CI/CD, runtime and feedback loops
  • Selecting open-source and enterprise tooling (LiteLLM, Langfuse, Arize Phoenix, Promptfoo)
Gateway and Versioned Configuration Layer
  • Unified model gateway, fallback chains and virtual keys
  • Environment-scoped versioning for prompts, model parameters and tool schemas
Automated Evaluation and CI/CD Quality Gates
  • Running deterministic and LLM-as-a-judge metrics against a golden dataset
  • Automated pull-request regression gates that block quality regressions
Runtime Guardrails, Caching and Observability
  • Input/output guardrails and semantic/prompt caching
  • Distributed tracing, cost attribution and trace dashboard design
The Production Data Flywheel
  • Live traffic sampling, user feedback capture and error triage
  • Feeding production edge cases back into the eval suite and improvement cycle

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  • CI/CD Pipelines and Automated Quality Gates for AI and LLM Applications

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  • Continuous Online Evaluation and Drift Detection for Production LLMs

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