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Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas  spanned by multiple horizontal data pipelines, platforms, and algorithms.  We are unifying data science and data engineering, showing what really works to run businesses at scale.
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Thursday, May 19 • 4:00pm - 4:40pm
Optimizing Machine Learning Models

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In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications. We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine. We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.

Speakers
avatar for Scott Clark

Scott Clark

Co-founder and CEO, SigOpt
Scott is the Co-founder and CEO of SigOpt, an optimization software as a service company helping firms tune their machine learning models and complex simulations. Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp... Read More →


Thursday May 19, 2016 4:00pm - 4:40pm
Markov

Attendees (21)