I am on a learning streak again. The most prominent technology that keeps landing on my tray at present is, of course, Artificial Intelligence (AI).
AI is hot. Very hot. And overhyped. Everyone is an expert nowadays. Yeah, right. Not me.
Underneath that glossy veneer of the AI hype, there are much going on behind the scenes to make AI great. The 2 areas I have been involved in and practiced for a long time are data infrastructure a.k.a. storage, and data management. And both are playing prominent parts in the advancement of the AI ecosystem. This makes me very excited.
I am no expert, but learning from various sources is already telling me that AI is pushing both storage and data management harder than ever before, much harder than traditional enterprise on-premises use cases and even the cloud computing applications. I ask myself, “where do I start my learning again?” as I journal my process.
Storage performance in a Data Pipeline
Speed of how AI responds is Trust. The faster it is to the accurate and relevant responses will build trust in AI. To get to the speed that we want is not an easy thing, and storage a.k.a. data infrastructure is doing its part. I pick up my learning from understanding the AI pipeline. One early help comes from my friend, Gina Rosenthal, who attended the Solidigm‘s presentation at AI Field Day in February 2024. Her article, titled “Why storage matters for AI – Solidigm“, kickstarted my learning juices again.
I was particularly captured by this slide in Gina’s article. It defines the laborious path data takes to become useful for AI applications.
Storage and the 5 stages of AI Work (reference: Solidigm presentation on AI Field Day)
