This step operationalizes AI so it stays safe, reliable, and cost-effective after launch. The goal is to prevent the common failure mode where systems are technically “running” but quality drifts, costs spike, and incidents repeat.

By the end of this step, you will have a practical operating model for MLOps and LLMOps in production, including monitoring, evaluation on live traffic, guardrails and red teaming as ongoing work, and the runbooks needed to respond when things go wrong.

Introduction

Step 1: MLOps Foundations — Make AI Shippable and Operable

Step 2: Operating GenAI at Scale — DevOps, DataOps, MLOps, LLMOps

Step 3: Monitoring AI Systems — Trace Behavior, Latency, and Cost

Step 4: Evaluate Generative AI Systems in Production (Faithfulness, Relevancy, Retrieval)

Step 5: Guardrails + Red Teaming as Ongoing Operations

Bonus: Runbooks, Rollbacks, and Operational Discipline

FAQs on Evaluation, Testing & Monitoring in Production