Leveraging Graphs for Data Preparation and Feature Engineering for Domo AutoML

The process of preparing data for use in predictive models is often a significant barrier to successful deployment. Richer, more informative datasets tend to be more complex, making the engineering of features from the raw data cumbersome and opaque to business stakeholders. A novel solution is the use of a flexible database in the background that can accommodate complex relationships within the data while also allowing for transparent feature engineering. In this discussion, we hope to demystify feature engineering and data preparation for data science efforts while also demonstrating how a graph database can make model building more efficient and more transparent to business stakeholders.

In this session you’ll learn how to:

  • Ingest complex relational tables into a graph database
  • Leverage graphs for transparent data manipulation and feature engineering for machine learning
  • Convert data science into a business-friendly process using Domo AutoML
  • See the benefits of Domo’s “polyglot” data ingestion capabilities using connectors

Dr. Lee Hong, Director of Data Science, Graphable
Rebecca Rabb, Consultant, Graphable

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