The most practical way to improve our machine learning predictions right away is using graph algorithms for connected feature extraction. We’ll quickly dive into creating a machine learning pipeline and tips on training and evaluating a model for link prediction – integrating Neo4j and Spark in our workflow. We’ll look at an example using several models to predict future collaborations and show measurable improvements using graph based features.
Amy manages the Neo4j graph analytics programs and marketing. She loves seeing how our ecosystem uses graph analytics to reveal structures within real-world networks and infer dynamic behavior. Amy has consistently helped teams break into new markets at startups and large companies including EDS, Microsoft and Hewlett-Packard (HP). She most recently comes from Cray Inc., where she was the analytics and artificial intelligence market manager. Amy has a love for science and art with an extreme fascination for complexity science. When the weather is good, you’re likely to find her cycling the passes in beautiful Eastern Washington.