Join us to hear about new supervised machine learning (ML) capabilities in Neo4j and learn how to train and store ML models in Neo4j with the Graph Data Science library (GDS).

Most data science models ignore network structure, but graph technology helps create highly predictive features to ML models, which increase accuracy and answer complex questions based on relationships. The latest GDS update (v1.5) provides a new end-to-end model-building pipeline entirely in Neo4j so you can take advantage of state-of-the-art ML techniques and continually update your graph – all without leaving Neo4j.

In this session, we’ll walk through how to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph or incoming graph data. You’ll also hear about other recent updates including new graph algorithms and memory optimization.

Instructor's Bio

Dr. Alicia Frame

Director of Product Management, Graph Data Science, Neo4j

Alicia Frame is the lead product manager for data science at Neo4j. She's spent the last year translating input from customers, early adopters, and the community into the first truly enterprise product for doing data science with graphs: Neo4j's Graph Data Science Library. She has a phd in computational biology from UNC Chapel Hill, and her background is in data science applications in healthcare and life sciences.She's worked in academia, government, and the private sector to leverage graph techniques for drug discovery, molecular optimization, and risk assessments -- and is super excited to be making it possible for anyone to use advanced graph techniques with Neo4j.


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    ON-DEMAND WEBINAR: Supervised Graph Machine Learning Now in Neo4j

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