Machine learning provides predictive models which a business can apply in countless ways to better understand its customers and operations. Since machine learning was first developed with flat, tabular data in mind, it is still not widely understood: when does it make sense to use graph databases and machine learning in combination? This talk tackles the question from two ends: classifying predictive analytics methods and assessing graph database attributes. It also examines the ongoing lifecycle for machine learning in production. From this analysis it builds a framework for seeing where machine learning on a graph can be advantageous.’