Aimed at "dramatically" improving the productivity of deep learning developers, the company's Determined AI Platform tightly integrates all of the features that a deep learning (DL) engineer needs to train models at scale. The AI infrastructure platform, says the company, manages users' heterogeneous hardware and optimizes their GPU resource utilization, and now powers teams of DL engineers and large GPU clusters in industries like pharmaceutical drug discovery, adtech, industrial IoT, and autonomous vehicles, and is now ready for widespread adoption.
Up to now, says the company, except for tech giants like Google, Facebook, and Microsoft – which have invested massive resources and expertise to build proprietary, AI-native internal infrastructure – lack of software infrastructure has been a fundamental bottleneck in achieving AI's immense potential. For everyone else who doesn’t have access to this infrastructure, building practical applications powered by AI remains prohibitively expensive, time-consuming, and difficult.
"We started Determined AI three years ago to bring AI-native software infrastructure to the broader market," says the company in a blog post announcing the move to open source. "Working closely with cutting-edge deep learning teams across a variety of industries, a clear narrative emerged: without better infrastructure, training deep learning models at scale remains extremely difficult, as organizations move from research to production."