Learn how Fivetran’s automated data movement platform allows you to quickly set up a relational database connector to the Databricks Lakehouse to move a wine quality dataset over to the lakehouse and ensure that it’s ML-ready. Then you’ll see how to use Databricks and AutoML to run classification experiments on the dataset to generate models for wine quality predictions based on a variety of parameters, including citric acid, ph, residual sugar, and sulphates. An extra bonus is that you don’t have to be a data engineer, ML engineer, or a wine expert to deliver quick value with this tech stack and approach.

University of California Irvine wine quality dataset: https://archive.ics.uci.edu/dataset/186/wine+quality

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