Presentation: The Infrastructure Challenge Behind Production AI
Production AI isn't failing because models are bad. It's failing because infrastructure teams weren't ready.
Why it matters
As AI moves from research to production, the bottleneck shifts from model capability to operational resilience. Engineering leaders must rethink architecture decisions to avoid catastrophic outages at scale — this is now table-stakes competitive advantage.
The key facts
10 to knowFocus on production database reliability under constant pressure, not model building
Architectural decisions now separate gracefully-scaling teams from those facing outages
Engineering leaders must rethink infrastructure approach for production AI
Panel includes infrastructure experts from industry (Simerus Mahesh, Alex Infanzon, Meryem Arik, Luca Bianchi, Renato Losio)
Focus: production database reliability under constant pressure from AI workloads
Key insight: model building is solved; production maintenance is the emerging challenge
Panelists include infrastructure and ML systems experts (Simerus Mahesh, Alex Infanzon, Meryem Arik, Luca Bianchi, Renato Losio)
Architectural decisions are now the primary differentiator for scale vs. outages
Published on InfoQ (technical/architecture audience)
Topic bridges infrastructure, ML ops, and engineering leadership decisions
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: The panelists explain the realities of running AI systems reliably at scale. While building models is solved, maintaining production databases under constant pressure is not. They discuss the emerging architectural decisions separating teams that scale gracefully from those facing catastrophic…