Real-time analytics is no longer optional – organizations expect immediate, intelligent insights from their data. Microsoft Fabric’s Real-Time Intelligence experience brings together event streaming, KQL databases, and low-latency reports, but its true potential emerges when you enrich those streams with machine learning tailored to your business.

This session explores how to build an end-to-end real-time analytics solution in Fabric and extend it with a custom machine learning model designed specifically considering the data patterns. We’ll demonstrate how to ingest and transform high-velocity data using Event streams and KQL, operationalize a ML solution using Fabric Data Science capabilities, and integrate model scoring directly into your real-time pipelines. Beyond anomaly detection, we’ll show you how the model can classify incoming data points and generate actionable recommendations – enabling automated decisions, alerts, and intelligent workflows.

Expect a practical, demo-driven journey covering architecture and lessons learned from real projects. Whether you’re modernizing existing streaming workloads or building new intelligent applications, you’ll leave with a clear blueprint for combining Fabric’s real-time engine with the flexibility of custom ML solutions – unlocking insights and actions that built-in models alone can’t deliver.