Data Science in Microsoft Fabric

Microsoft Fabric's perhaps least well-known area of functionality is its Data Science workload for AI. Unlike other parts of Fabric, which effectively integrate Azure technologies, Fabric's Data Science capabilities are not derived from Azure Machine Learning. Instead, Fabric embeds a variety of open source technologies, leverages the Apache Spark and notebook components used by Fabric's Data Engineering workload, and even has special semantic integrations with Power BI.

As a modern data stack professional or data scientist, you can get a ton of value from Fabric Data Science, but you have to know how to use it and this session is designed to you up to speed. The session is presented by Andrew Brust, Microsoft Data Platform MVP, Fabric early adopter program participant, and a tech journalist and industry analyst focused on data and analytics.

You will learn:
* How to set up Fabric Experiments, Models, and model versions using Fabric's MLflow capabilities
* AutoML basics using FLAML, SynapseML and MLflow Tracking
* How to score data against ML models using Spark SQL and the PREDICT function
* Integration of Power BI semantic models into Fabric Data Science
* Use of Fabric's Data Wrangler and its Python code gen capabilities