A machine learning technology company co-founded by Ameet Talwalkar, Assistant Professor at Machine learning department at Carnegie Mellon University School of Computer Science, will join Hewlett Packard Enterprise (HPE).
Determined AI, a San Francisco-based startup, is building software that trains AI models faster and at scale using its open-source machine learning platform. Talwalkar is Chief Scientist at Determined AI, which he co-founded in 2017 with Neil Conway and Evan Sparks.
“We are excited about the opportunity to partner with HPE to deliver co-designed software and hardware and address some of the company’s most pressing challenges,” the founders written in a blog post announcing the acquisition. “HPE shares our vision that implementing an open standard for AI software infrastructure is the fastest way for the industry to realize the potential of AI. “
The founders wrote that HPE will continue to expand Defined AI’s training platform as an open source project. The platform allows engineers to easily implement and train machine learning models to deliver faster and more accurate information from data in almost any industry. For example, the Defined AI platform accelerated the training of a machine learning model for drug discovery from three days to three hours.
“AI-powered technologies will play an increasingly critical role in transforming data into readily available and actionable information to fuel this new era,” said Justin Hotard, senior vice president and general manager of the High Performance divisions. Computing and Mission Critical Solutions from Hewlett Packard. “Determined AI’s unique open source platform enables ML engineers to build models faster and deliver business value sooner without having to worry about the underlying infrastructure. “
Talwalkar obtained his doctorate. and a master’s degree in computer science from the Courant Institute at New York University and joined the CMU faculty in 2018. He recently received a Early Career Development Program (CAREER) Faculty Award of the National Science Foundation to help automate the design of new deep learning models for a diverse set of tasks in the physical and social sciences.