ai-mlops

# MLOps & LLMOps - Production Operations Hub **July 2026 posture:** version every changeable artifact, gate every release with a regression-eval suite in CI, instrument the whole path with OpenTelemetry (pin GenAI convention version — the spec now lives in its own repo and is still evolving), treat tool/RAG context as untrusted input, and ship rollback plus incident playbooks before launch. This skill is the execution hub for **operating AI systems in production**: - **Classical ML ops**: ingestion, registries, feature stores, drift, retraining, promotion - **LLMOps**: serving, prompt/config lifecycle, online evals, cost controls, safety gates - **Agent runtime ops**: tracing, tool governance, approval paths, MCP-aware telemetry, rollback - **Governance**: privacy, supply chain, auditability, AI Act readiness, safety incident handling Use this skill for **production architecture, release gates, monitoring, incidents, and governance**. Use adjacent skills for modelling, retrieval depth, agent design, or inference internals. ## When To Use This Skill Activate this skill when the user asks for: - Deploying an ML, LLM, RAG, or agent-backed system to production - Designing serving, batch, hybrid, or multi-region runtime architecture - Adding observability, drift detection, alerting, retraining, or release gates - Writing incident runbooks, rollback plans, or go/no-go checklists - Hardening an AI system against prompt injection, RAG poisoning, tool abuse, or data leakage - B...