AI is transforming the way we build and manage Power BI semantic models, but only if the foundations are right. In this full day workshop, you will follow the complete lifecycle of a production ready semantic model in Microsoft Fabric: from early planning decisions through to monitoring in production.

You will start by choosing the right storage mode and designing a star schema that works for both your reports and AI tools like Copilot. Then you will build a real semantic model using modern AI assisted tooling, including MCP servers that let you create tables, measures, and relationships through natural language.

But building is just the beginning. You will learn how to prepare your model so that AI features actually work, adding synonyms, descriptions, and linguistic metadata that make Copilot and Q&A shine. You will then tackle DAX performance head on, using AI powered analysis to find and fix slow queries before your users notice.

The day rounds out with enterprise essentials: designing scalable security with row level and object level security patterns, and setting up the monitoring and alerting that keeps your models healthy in production.

By the end, you will have the skills and confidence to plan, build, optimise, secure, and monitor semantic models that make Power BI reports run blazingly fast, with AI as your co pilot every step of the way.

**What you'll learn**:

- Plan and design semantic models that are optimised for both performance and AI
- Build models using AI-assisted tooling and MCP servers
- Prepare your model with synonyms, descriptions, and metadata so Copilot works brilliantly
- Identify and fix slow DAX using AI-powered performance analysis
- Implement scalable row-level and object-level security
- Set up monitoring, alerting, and lifecycle management in Microsoft Fabric

By the end of this workshop, every attendee will be well-equipped to **plan,
build, optimise, secure and monitor** a semantic model in Microsoft Fabric —
so that the Power BI reports sitting on top run really, really fast.