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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. Your questions will be answered throughout the webinar!
