"Translating AI Capabilities Into Better Business Results"

Machine learning is a technique for making predictions, but businesses are broadly struggling to turn these predictions into better big-picture business results. The problem is that data scientists typically do little to rigorously the business rules that they apply to their machine learning processes. This talk shows techniques to use machine learning outputs more efficiently and deliver more bottom-line value from the same predictions.

Dan is the CEO of Decision AI. Decision AI helps data scientists deliver better business results from their existing machine learning models. Before founding Decision AI, Dan worked for Google where he created the world's second largest AI education platform. He has contributed to the Keras and Tensorflow libraries for deep learning, finished 2nd (out of 1353 teams) in the $3million Heritage Health Prize data mining competition, and he has supervised data science consulting projects for 6 companies in the Fortune 100. Dan has a PhD in Econometrics.