Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Hewlett Packard Enterprise said it acquired Determined AI, a startup with an open-source machine learning platform to train models faster. HPE said it will integrate Determined AI's technology with its artificial intelligence and high-performance computing portfolio. Terms of the deal weren't disclosed. According to HPE, Determined AI will help it give businesses the ability to create models faster and deliver business value without worrying about infrastructure underneath.