Mix-and-match approaches for visualizing data and interpreting machine learning models and results.
Patrick Hall is a senior data scientist and product engineer at H2o.ai. Patrick works with H2o.ai customers to derive substantive business value from machine learning technologies. His product work at H2o.ai focuses on two important aspects of applied machine learning, model interpretability and model deployment. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.
Prior to joining H2o.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the 11th person worldwide to become a Cloudera certified data scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.
Measure your model’s business impact, not just its accuracy.
Tips for using machine learning models in regulated industries.