Join Barry for an insightful demonstration of a Data Mesh-inspired architecture leveraging a comprehensive suite of features in Microsoft Fabric. This session showcases a complete, end-to-end data engineering and data science experience in Fabric using the iconic Titanic dataset to build a machine learning model predicting passenger survival.

Starting with data ingestion and exploratory data analysis, Barry will guide you through the essential stages of data cleansing, wrangling, and semantic model creation. The session will culminate in training, evaluating, and deploying a robust ML model, illustrating the practical application of key DataOps principles throughout the lifecycle.

Discover how Microsoft Fabric empowers you to create impactful data products, enabling incremental development of your data capabilities in a "microservices for data" architecture. Don't miss this opportunity to learn how to harness the power of Microsoft Fabric to build scalable, efficient, and innovative data solutions.

Key Fabric features in demo: task flows, lakehouse, notebooks (key packages: MLflow, Sempy "semantic link", AzureLogHandler, Great Expectations), pipelines (includes new Teams activity), semantic model, report, experiment, model, environment, source control.

Updated / improved version of demo and talk given at SQLbits 2024, overall score 8.38 out of 9. Comments: "Brilliant session, really enjoyed it and learnt a lot. Liked business context at the start.", "Very nice demo!", "Brilliant demos and easy to follow.", "A really informative session about Fabric", "A good structure of presentation."