University of Oxford Spin-out Mind Foundry Launches Machine Learning Platform

By on

According to a recent press release, “Mind Foundry, a technology spin-out from the University of Oxford’s Machine Learning Research Group (MLRG), today announced the commercial launch of a revolutionary humanised machine learning platform. For the first time the new cloud-based platform allows anyone, of any technical ability and in any size of organisation, to swiftly unlock the full value of ever increasing volumes of data to make decisions on complex business issues without the need for data scientists. The platform was developed through work with some of the world’s largest investment firms, telecommunications providers, manufacturers and heavy industry companies. Organisations can proactively solve business problems by easily leveraging the predictive power of their existing data. The platform automatically builds appropriate machine learning solutions for business problems in minutes or hours, rather than weeks or months, and provides full transparency and auditability of solutions.”

The release goes on, “Problem owners are guided through simple steps to develop and deploy models. Along the way, the platform gives guidance and advice to enable continuous improvements, discovery of actionable insights and a complete understanding of machine learning derived results. Mind Foundry recently appointed Paul Reader to spearhead the launch of the new platform. Paul Reader is bringing a wealth of go-to-market strategy execution experience gained from working as a consultant and an executive with other SaaS software start-ups to make Mind Foundry’s advanced machine learning capabilities accessible to a wide range of business users across functional departments and spanning a number of industries such as finance, telecommunications and life sciences.”


Image used under license from

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept