The second installment of HPCwire’s 2-part BioTeam interview continues the conversation with Ari Berman, Chris Dagdigian and Aaron Gardner as we take a hard look at AI and deep learning, as well as performance and cost improvements within both cloud and storage.
Part 2 delves beyond the AI marketing fever and into the gritty facts and potential benefits of the underlying technology. The interest and transformational potential are not without merit, there are production use cases and profound success stories in life sciences research being driven by AI and deep learning.
The interview also covers the narrowing divide of cloud costs and services between different cloud providers and how initiatives in the Federal space are further enabling cloud adoption in the life sciences. The group discuss a number of cloud use cases, including the concept of the data commons for sharing public datasets and how Pharma is using the cloud as ‘neutral territory’ for collaborative engagements between commercial entities.
Looking forward, AWS’ support for parallel filesystems has added a boost to HPC use cases, and while cloud storage costs are still a significant issue, the Cloud is undoubtedly enabling significant advances in dynamic and data-intensive scientific research and this is only set to grow.
Part 2: HPC in Life Sciences Part 2: Penetrating AI’s Hype and the Cloud’s Haze
See also: HPC trends in Life Sciences, 2019 – PART 1