Featurization is one of the most difficult problems in machine learning, just behind data wrangling in terms of the time it consumes. For many problems, featurization plays the largest role in determining model performance, greater even than choice of machine learning method. We’ll walk through how graph features engineered in Neo4j can be used in a supervised learning model trained with Amazon SageMaker. These novel graph features can improve model performance beyond what is possible with more traditional approaches.