About the talk
Modeling is a scientific process that requires experimentation to get right. But experimentation is only as effective as the combination of tools and techniques applied to it. During this session, SigOpt GM Scott Clark discusses an intelligent approach to AI experimentation, including how to design experiments, explore model parameter spaces and optimize model hyperparameters. This discussion will include guidance on techniques and tools that make this workflow more efficient, effective, and scalable. You will leave the session with a framework for experimentation, including considerations around metrics, parameters, architectures, runs, compute and hyperparameter search methods. And you will receive a fully executable notebook that comes with free SigOpt access to begin to explore these lessons on your own.
Scott Clark is the co-founder, former CEO, and current general manager of SigOpt, acquired by Intel in November 2020. Scott leads SigOpt's ongoing efforts to build a product and vision of an Intelligent Experimentation platform that accelerates and amplifies the impact of modelers everywhere. Scott has been applying optimal learning techniques in industry and academia for years in areas that include bioinformatics and production advertising systems. Before co-founding SigOpt, he worked on the Ad Targeting team at Yelp, where he led the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open-source Metric Optimization Engine (MOE). Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University. He also holds BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. He was recognized as one of Forbes' 30 under 30 in 2016.View the profile
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