# MLOps and LLMOps: Production AI Engineering, Observability, and Platform Architecture
Status: public
Confidence: medium (0.82) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR
MLOps and LLMOps: Production AI Engineering, Observability, and Platform Architecture: MLOps and LLMOps operationalize machine-learning and language-model systems through testing, deployment, monitoring, evaluation, and governance.

## Core Explanation
Production ML has hidden technical debt because data, models, code, infrastructure, and feedback loops interact. LLMOps adds prompt, retrieval, safety, evaluation, and model-update concerns to the broader MLOps discipline.

## Further Reading

- [Hidden Technical Debt in Machine Learning Systems](https://papers.nips.cc/paper_files/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html)
- [The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction](https://research.google/pubs/the-ml-test-score-a-rubric-for-ml-production-readiness-and-technical-debt-reduction/)
- [AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)