Automate building ML apps with Databricks, AutoML and Fivetran

Fivetran, Databricks, and AutoML streamline the process of building machine learning applications in the Databricks Lakehouse by automating data movement and efficient model creation. The text explains how to set up a relational database connector to the Databricks Lakehouse using Fivetran and move a wine quality dataset over for classification experiments and predicting wine quality based on various parameters. It also highlights the importance of having high-quality, usable data and how Fivetran's automated data platform helps achieve this by centralizing data and modernizing data infrastructure. The text concludes with an overview of managing source changes and schema drift, starting the initial sync from PostgreSQL to the Databricks Lakehouse, and building a wine quality application using Databricks AutoML.

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