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LLMOps CI/CD Pipeline & Model Evaluation

End-to-end pipeline for model versioning, automated evaluation, canary deploys, and rollbacks — treating prompts and models like code.

BuildTestDeployMonitor SaaS / Technology
90%
Evaluation Automated
80%
Manual Effort Saved
100+
Models Deployed

Business Problem

Prompt edits, model upgrades, and retrieval changes shipped without behavioral verification. Every release was a production gamble, and regressions were discovered by users instead of pipelines.

Architecture Overview

A deployment pipeline where every change runs a golden-dataset evaluation gate, canaries to a fraction of traffic with live scoring, and rolls back automatically on regression.

  1. 1 Source control (code + prompts + model IDs)
  2. 2 Contract tests + smoke evals
  3. 3 Golden-dataset evaluation gate
  4. 4 Canary deploy (5% traffic)
  5. 5 Live evaluation + auto-rollback
  6. 6 Full release promotion

All components are deployed with private networking, scoped IAM roles, and encryption at rest and in transit.

Key Design Decisions

  • Version everything together. A deployable unit is code + prompt templates + model IDs + index version + eval baseline, tagged as one release.
  • Gate on regression, not just thresholds. Deploys fail on quality drops relative to the production baseline, not only absolute floors.
  • Judge-result caching. LLM-judge results cached by (case, prompt-version, model-version) keep evaluation costs sane.

Technology Stack

GitHub ActionsMLflowAirflowDockerPythonGreat Expectations

My Role

  • Designed the evaluation gate architecture and golden datasets.
  • Built CI workflows for contract tests, evals, and canary deploys.
  • Implemented model/prompt registry and release tagging.
  • Established DevSecOps controls with 100% regulatory compliance.

Measurable Impact

  • 90% Evaluation Automated
  • 80% Manual Effort Saved
  • 100+ Models Deployed

Challenges & How I Solved Them

  • Flaky judges: Property-based assertions and consensus judging stabilized evaluation verdicts.
  • Eval cost: Tiered suites — 20-case smoke in minutes, full golden set gated on merge.
  • Behavioral drift: Live canary scoring caught provider-side model updates offline evals missed.

Lessons Learned

  • Treat every prompt edit like a schema migration.
  • Evaluation suites must grow from real production failures.
  • Rollback speed matters more than gate perfection.