This book is a comprehensive guide for data professionals and AI practitioners looking to build modern, end-to-end AI and analytics solutions using Microsoft's powerful Fabric platform. Whether you're launching a new enterprise data strategy or modernizing an existing pipeline, this book offers a practical roadmap to implementing scalable, cloud-native AI in your organization.
The book begins by explaining the role of Microsoft Fabric in the analytics framework and lays out a clear foundation for building AI-driven solutions. Readers are introduced to Fabric's component architecture--Administration, Bicep, Capacity, Data-stores, Experiences, and Flows--along with practical steps to build ingestion pipelines, engineer data, and deploy machine learning models. Subsequent chapters walk through real-world use cases, showing how Fabric simplifies complex workflows by unifying capabilities like data ingestion (OneLake, Mirroring, Shortcuts), serverless Spark-based AI Notebooks, and model training pipelines. Readers will also learn how to create and manage AI Functions and Skills, integrate AI directly into Power BI reports, and harness the Semantic Link library for programmatic data access. The book concludes by exploring how to apply MLOps and LLMOps principles within Microsoft Fabric, enabling continuous delivery and governance for enterprise AI applications. With a strong focus on both the technical architecture and business outcomes, this book empowers readers to turn Fabric's unified platform into real, enterprise-grade AI solutions.
Whether you're a data engineer, AI developer, or analytics leader, this guide will help you unlock the full potential of Microsoft Fabric in your enterprise AI journey.
What you will learn:
Who this book is for:
This book is intended for BI professionals, architects, and decision-makers building AI solutions with Microsoft Fabric. It assumes basic knowledge of BI and analytics. Data engineers and developers will learn how to streamline AI and ML workflows using Fabric's unified platform.
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