Abstract: 

AutoML is coming to deep learning. Traditional deep learning models would take data scientists weeks to code and tune. Learn how to take multimodal datasets, mixing of tabular and unstructured data (images, audio, video), and create accurate deep learning models in under 10
minutes with DataRobot. AutoML lets users have access to the latest frameworks like Keras, but with a push of a button be able to access transparent interpretability tools like feature impact, partial dependence, and prediction explanations. In this session, we will reveal some
recent breakthroughs in deep learning and walk through some detailed examples from data to deployment.

Bio: 

Ben Taylor has over 16 years of machine learning experience. After studying chemical engineering, Taylor joined Intel and Micron and worked in their photolithography, process control, and yield prediction groups. Pursuing his love for high-performance computing (HPC) and predictive modeling, Taylor joined an artificial intelligence hedge fund (AIQ) as their HPC/AI expert and built out models using a 600 GPU cluster to predict stock movements based on the news. Taylor then joined a young HR startup called HireVue. Taylor built out their data science group, filed 7 patents, and helped to launch HireVue’s AI insights product using video/audio from candidate interviews. That work allowed Taylor’s team of PhD physicists to help pioneer anti-bias mitigation strategies for AI. In 2017 Taylor co-founded Zeff.ai with David Gonzalez to pursue deep learning for image, audio, video, and text for the enterprise. Zeff was acquired by DataRobot