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.