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Chapter 72: Capstone — End-to-End LLMOps

Pull everything together. You run an end-to-end pipeline—data, training, evaluation, safety, serving, and integration—then package the final llmops skill.

Goals​

  • Execute the full LLMOps pipeline on the Task API use case
  • Enforce evaluation and safety gates before deployment
  • Deploy and integrate the custom model with your agents
  • Finalize and document your comprehensive LLMOps skill

Capstone Flow​

  1. Data curation and validation
  2. Fine-tuning (persona/function calling as needed)
  3. Merging/optimization (optional)
  4. Safety alignment and evaluations with quality gates
  5. Serving with versioning/traffic controls
  6. Integration with agent frameworks and MCP
  7. Monitoring, rollback, and documentation of the skill

Outcome & Method​

You finish with a production-ready custom model powering your Task API agents plus a documented, reusable LLMOps skill that encodes the entire lifecycle.

Prerequisites​

  • Completion of Chapters 61-71