Implementing robust batch scoring in Fabric, including patterns for scheduling, monitoring, and integrating predictions into downstream systems.

Training, evaluating, and registering an ML model inside Fabric, including how this ties into MLOps and downstream consumption.

A hands-on walkthrough of exploring churn data with notebooks and visualisation in Fabric, aimed at data scientists and analytics engineers.

How to ingest data from external systems into Fabric—covering shortcuts, pipelines, and best practices for staging and governance.

An overview of Microsoft Fabric as a unified analytics platform, and how its DS & AI experience ties together storage, engineering, ML, and BI.