, a technology spin-out from the University of Oxford’s Machine Learning Research Group (MLRG), has unveiled 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. 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.
“This launch is the culmination of years of hard work and commercial development from our world-class team of machine learning researchers and software engineers,” said Professor Stephen Roberts, Co-founder and Chief Scientific Advisor, Mind Foundry. “At a time when data science skills are in short supply, Mind Foundry aims to create 1,000 ‘citizen data scientists’ by 2020 and ensure humans are always ‘in the loop’ when it comes to machine learning.”