AI Infrastructure: Getting More From What You Already Have
Most enterprise infrastructure teams aren’t waiting on a clean-slate architecture. They’re trying to make what they already own work harder and faster.
That pressure is reaching a turning point. It’s the challenge running through everything we’ve pulled together this month.
The teams making the most progress right now eliminate redundant data copies before they become a performance problem, making data accessible to distributed GPU environments without re-architecting storage, and don’t treat neoclouds and edge resources as afterthoughts.
None of it requires waiting on the next budget cycle. Most of it starts with getting clear on where data actually lives and where the friction is.
This month’s news approach that problem from a few different angles:
- On the platform side, our two-part blog series breaks down how the definition of “best-in-class” infrastructure is shifting, from raw capacity to how data is accessed, governed, and activated across silos without forcing migrations.
- In the podcast roundup, our team and guests cover the same challenge across industries, from metadata management at the Space Force, to why life sciences teams are in a uniquely tough spot, to what a former UK Labour Party CDO thinks “data sovereignty” actually requires in practice.
- And if life sciences or research infrastructure is your world, read Adam Marko’s piece on building an AI-ready data foundation for scientific research (following Bio-IT World last week in Boston).
If something here resonates or there’s a challenge your team is working through that we should cover next month, hit reply.
Technical Blog Series
Data Platform Innovation
As AI adoption expands beyond specialized HPC teams into mainstream enterprises, the definition of “best-in-class” is shifting from infrastructure to how data is accessed, governed, and used.
In this two-part blog series, find out more about the latest updates to the Hammerspace Data Platform that allows organizations to:
- Deliver unified, high-performance access and orchestration to data across existing silos, sites, and cloud storage from any vendor
- Enable existing investments to deliver new value
- Consolidate fragmented data environments into a coherent, intelligent whole
- Activate a dynamic AI pipeline without being forced into disruptive data migrations or disrupting current data operations or user access
Data Podcast Roundup
This month featured our team in conversation with the leading experts on topics ranging from AI data sovereignty, data gravity, metadata management at scale. Grab your headphones and listen to:
Metadata at the Space Force and Beyond: Hammerspace’s CEO speaks with former Space Force CDO Mark Brady to explore metadata management at scale, from outer space to the most complex organizations in the world (DM Radio).
Adam Marko on AI-Ready Life Sciences Data: Discover what “data orchestration” means when building an AI-ready data foundation, the tiered storage patterns that help teams keep AI and HPC workloads moving, and why life sciences are in a uniquely tough spot (Trends from the Trenches).
A Sovereign Wealth Fund for Data?: James Robson, former CDO for the UK’s Labour Party, on what data sovereignty means, and how companies can achieve it (DM Radio).
The Future of AI Will Belong to Companies that Make Data AI-Ready Now: Find out from SHI’s Jack Hogan why so many enterprise AI projects stall, what business leaders often get wrong, and how organizations can better prepare for long-term success (Data Unchained).
From Data Chaos to Discovery
Building the Data Foundation for AI-Ready Scientific Research
Following the Bio-IT World conference last week in Boston, Life Sciences CTO Adam Marko discusses how AI is evolving from a tool into the operational core of research and enterprise decision-making.
Learn how to design an AI-ready data strategy, enable continuous data delivery, automate orchestration/lifecycle management, and shift from storage-centric to data-centric thinking.
