In this, the final episode of our Strata Data Conference series, we're joined by Zachary Hanif, Director of Machine Learning at Capital One's Center for Machine Learning.
Zach led a session at Strata called "Network effects: Working with modern graph analytic systems," which we had a great chat about back in New York. We start our discussion with a look at the role of graph analytics in the machine learning toolkit, including some important application areas for graph-based systems. We continue with an overview of the different ways to implement graph analytics, with a particular emphasis on the emerging role of what he calls graphical processing engines which excel at handling large datasets. We also discuss the relationship between these kinds of systems and probabilistic graphical models, graphical embedding models, and graph convolutional networks in deep learning.