MLOps, AIOps, AI Observability System Design & Interview Preparation

Your model passed every test, shipped at 94% accuracy, and three weeks later it was quietly wrong — and no dashboard noticed.

That is the failure this book is built to prevent. The model is only 5–10% of a production AI system. The other 90% — data that drifts, models that decay in silence, costs that balloon, and predictions you cannot verify at deploy time — is the part that decides whether AI creates value or quietly destroys it. This is the field guide to that 90%.

Written for senior and staff engineers, platform architects, and anyone preparing for MLOps, LLMOps, or AIOps interviews, this is a system-design-level guide to operating AI in 2026 — classical machine learning, large language models, and the autonomous operations now emerging on top of both.

Inside you'll master:

The 2026 MLOps lifecycle end to end: data and feature management, experimentation, CI/CD/CT, deployment strategies, and the four-layer monitoring model.

AI observability done right — drift detection, data-quality signals, LLM tracing, and evaluation — the discipline of sensing failures before they become outages.

LLMOps vs MLOps vs AIOps: what actually changes when the artifact is a language model, and how the three disciplines converge onto one control plane.

FinOps for AI: cost attribution and chargeback, the compute pricing spectrum, spot training, and LLM token economics.

Edge and federated MLOps: model compression, on-device runtimes, over-the-air fleet rollout, and privacy-preserving training.

Governance, compliance, and risk — model cards, audit trails, policy as code, and the EU AI Act.

Autonomous and agentic AIOps — self-healing pipelines and the road ahead.

Built to make you interview-ready. Every chapter ends with five detailed interview questions and worked answers spanning system design, production operations, debugging, and real-world case studies — the exact questions asked at senior and staff level.

Learn by running real code. The final part grounds everything in one open-source reference implementation: a multi-tenant Retrieval-Augmented Generation platform wired for observability with Langfuse tracing, DeepEval evaluation, and OpenLLMetry telemetry — FastAPI, Next.js, PostgreSQL + pgvector, LangGraph, and local Ollama models. Clone it, break it, and instrument it yourself.

Every chapter pairs deep concepts with an architecture diagram, comparison tables, production-grade code, and hard-won field notes.

You cannot operate what you cannot sense. Learn to sense it.

👉 Companion code: github.com/lamhotsiagian/llm-ops-observability