{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/mlops-llmops",
  "headline": "MLOps and LLMOps: Production AI Engineering, Observability, and Platform Architecture",
  "description": "MLOps and LLMOps are the engineering disciplines that bridge the gap between a research notebook and a reliable, monitored, cost-effective production AI system. As enterprises deploy LLMs at scale, LLMOps extends traditional MLOps with prompt versioning, guardrail monitoring, hallucination detection, and cost-per-call optimization — making AI operations a $10B+ market by 2026.",
  "dateCreated": "2026-05-24T02:49:13.635Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Machine learning operations landscape: platforms and tools",
      "sameAs": "https://link.springer.com/article/10.1007/s10462-025-11164-3"
    },
    {
      "@type": "CreativeWork",
      "name": "AI Observability for Large Language Model Systems: A Multi-Layer Taxonomy",
      "sameAs": "https://arxiv.org/abs/2604.26152"
    }
  ]
}