While cloud computing and storage continues to grow quickly, spending on IT infrastructure for the data center continues to grow as well. A lot of that growth is being driven by the need to store large quantities of unstructured data for AI and analytics. Enterprises are reporting that they are increasingly looking at parallel file systems and scale out architectures to accommodate the need for the performance and capacity required to meet the needs of compute-heavy applications. National laboratories, government agencies, and universities have used use parallel file systems for accelerated workflows for many years, and now many enterprises urgently need them too.

It's tempting to think that you might just upgrade your current enterprise storage solution to achieve your AI business goals but, says Kurt Kuckein of high performance storage vendor DDN, that's putting you on the path to failure. Join him and Eric Burgener of IDC as they explain why existing enterprise storage architecture is not going to realise your AI and analytics ambitions, what enterprise supercomputing looks like and why parallel file systems are a must-have to achieve success in AI and analytics at scale – and how to go about adoption the right way. In our live Regcast they will be tell the Reg's Tim Phillips about:

• Why parallel file systems are essential to advanced computing in the enterprise
• The path to enterprise AI at-scale failure
• The parallel file systems path to success