Enterprise AI is now a boardroom mandate, but moving from pilots to production remains a major challenge. Many organizations are trying to determine whether their infrastructure can support AI at scale without driving up costs, hitting power constraints, or locking into the wrong architecture. Training isn't inference. Fine-tuning isn't RAG. Getting these distinctions wrong can cost millions.

Join Eric Kavanagh (DM Radio) for an interactive live webcast with Mark Madsen (Third Nature), Denise Muyco (RAVEL), and industry leaders as they break down how different AI workloads place very different demands on compute, power, and orchestration — and what actually changes when moving from testing to enterprise production.

This session will equip AI and infrastructure leaders with practical guidance to make confident, future-ready infrastructure decisions.

Attendees will learn: * The key questions to ask when planning AI for production * How infrastructure needs differ across training, inference, and experimentation * How to test, validate, and scale AI workloads * How to identify bottlenecks that hurt performance and ROI * Why smart infrastructure strategy is critical to scaling AI successfully

If you're responsible for AI or infrastructure strategy, this is the conversation that could save — or justify — your next $10 million decision.