This step focuses on the architecture patterns and platform foundations required to scale AI beyond a few deployments. The goal is to make AI systems repeatable, observable, secure, and cost-controlled as usage grows.

By the end of this step, you will understand the MLOps and LLMOps building blocks, how GenAI changes system architecture (including multi-agent patterns), and how to design for events, context, and integration so AI products can run reliably in production.

Introduction

Step 1: MLOps Foundations

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

Step 3: Multi-Agent Systems — What Changes for Architecture

Step 4: Manage Models & Event-Driven Architecture

Step 5: Build Context with Vectors, Graphs, Metadata & Master Data

Bonus: MCP — Connecting Agents to Tools and Enterprise Systems