SigOpt Raises $6.6 Million in Series A Funding
The startup has a machine learning platform that uses Bayesian techniques to improve model development and research.
San Francisco-based SigOpt has secured a $6.6 million Series A round of financing.
This is the second round of fund raising for the company, which raised $2 million at the beginning of 2015 from Andreessen Horowitz and Data Collective. Those two firms are back for this round of funding, joined by SV Angel, Stanford University and Blumberg Capital.
SigOpt has a platform that uses Bayesian-led machine learning optimization that allows hedge fund quants to improve their algorithmic-trading models and helps large banks enhance their risk models. The platform can be bolted on top of existing machine learning, AI and predictive analytics pipelines. It aims to cut down on the time and costs associated with traditional A/B testing by reducing the time and resources spent on fine tuning these models through the use of Bayesian techniques.
This round of funding will help the firm to expand its capabilities and grow its team.
On Monday, WatersTechnology will publish a deeper dive into what SigOpt is working on and why it's generating interest from investors.
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