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
Additional info and registration:
- More session details: Leveraging Graphs for Data Preparation and Feature Engineering for Domo AutoML
- All DP21 sessions: DP21 Agenda
- Register for conference & more conference details: Domopalooza Virtual Conference
Graphable delivers insightful graph database (e.g. Neo4j consulting) / machine learning (ml) / natural language processing (nlp) projects as well as graph and Domo consulting for BI/analytics, with measurable impact. We are known for operating ethically, communicating well, and delivering on-time. With hundreds of successful projects across most industries, we thrive in the most challenging data integration and data science contexts, driving analytics success.
Want to find out more about our Hume consulting on the Hume knowledge graph / insights platform? As the Americas principal reseller, we are happy to connect and tell you more. Book a demo by contacting us here.
Check out our article, What is a Graph Database? for more info.