SigOpt Named A Gartner "Cool Vendor" in AI Core Technologies

From the day we founded SigOpt, we set out to help data scientists and researchers across a variety of industries improve their modeling. Our technology automates the parameter tuning of predictive models such as neural networks through an ensemble of Bayesian optimization algorithms. This produces less expensive, faster, and more effective results than traditional approaches such as manual tuning (i.e. trial and error). SigOpt’s optimization platform helps experts automatically find the best configuration parameters for their models and better solve problems that range from oil and gas exploration to algorithmic trading strategies.

Today, we’re honored to share that Gartner has listed us in its Cool Vendor 2017 report for AI Core Technologies

First and foremost, we want to thank Gartner for recognizing the work we’ve accomplished thus far. We also want to congratulate the other impressive startups that are included on this year’s list! 

So, what exactly do we think makes us “cool”?

We’re biased, but our traction with our customers has been nothing short of astounding: Data scientists, engineers, and R&D pros are not easy to impress, but we’ve seen their surprise and delight firsthand when they use our optimization platform. We’ve been fortunate to work with these researchers at some of the world’s largest banks, major consumer brands, academic institutions and fast-moving hedge funds.  The impact our technology has had on these companies’ machine learning models - whether in fraud detection, algorithmic trading, or scientific experiments - has been huge. 

SigOpt utilizes techniques from the academic literature, across disparate fields of mathematics and statistical learning theory to tune our customers’ models. If companies are not already optimizing the configuration parameters of their models, they are forgoing significant performance and revenue gains. Data Scientists often overlook these optimizations, but only because traditional approaches like manual, grid, and random search waste so much time and resources. These strategies are expensive, time-consuming, and produce inferior results.

We believe Gartner’s inclusion of SigOpt in this year’s Cool Vendors 2017 report for AI Core Technologies is further validation to us that we’re doing something important. We’re excited by the opportunity to reach a wider audience. Stay tuned for more

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Gartner, Cool Vendors in AI Core Technologies, 2017, 16 May 2017

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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 Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.
Scott Clark, PhD