Summary

In this chapter, you learned about the data science process and data science project development life cycle, after which you learned about different capabilities in Microsoft Fabric that empower you at each step in this journey. You learned about all these capabilities by implementing an end-to-end data science project in Microsoft Fabric based on the regression model and also learned how to leverage advanced capabilities, such as using the model registry and tracking with MLflow, Semantic Link, AutoML, and SynapseML.

This whole experience is natively integrated and built into Fabric, giving you the power and flexibility to build end-to-end data science projects without having to switch to or learn about other technologies. This includes capabilities for data ingestion, data transformation, and feature engineering with Notebooks/Spark to training ML models in a distributed manner with SynapseML. Furthermore, it allows you to leverage different open source libraries such...