Automatic Data Science AI

In a recent competition for predicting consumer credit risk, AutoAI beat 90% of the participating data scientists. In this talk, I will share the methodologies behind the development of AutoAI and how it accelerates time to value with data science projects. Come listen how a combination of Data Science patterns are combined with years of research to develop a tool that can choose the best possible machine learning model automatically for you.
Who is this presentation for?
Data Science / AI / ML practitioners
Prerequisite knowledge:
Basic Data Science and AI
What you'll learn?
Methodologies to automate data preparation, model selection, feature engineering and hyper-parameter tuning

Jorge Castañón hails from Mexico City and received his Ph.D. in Computational and Applied Mathematics from Rice University. He has a genuine passion for data science and machine learning applications of any kind. For 10+ years he is been developing data science and AI models as well as algorithms to solve numerical optimization and regularized inverse problems. At IBM, Jorge is the Lead Data Scientist of the Machine Learning Hub World Wide.