Running enterprise applications in production is a lot different from the AI experiments many of us have been involved with so far. This episode of the Tech Field Day podcast, recorded prior to NetApp Insight 2025, features Ingo Fuchs from NetApp along with Gina Rosenthal, Glenn Dekhayser, and Stephen Foskett. AI applications often start as experiments with a limited data set, but once these are moved to production there are many critical decisions to be made. Data must be classified and cleaned, removing personal and financial data and proprietary information before it even reaches an LLM. Data also must be structured for embedding and vectorization prior to use by an LLM. And we have to ensure that data is up to date or the application will not serve the customer properly. Finally we have to consider whether it is proper and ethical to share and act on this data. Many of the challenges facing modern AI applications are similar to the historic issues faced by enterprise storage, and this is an area in which NetApp and their customers have decades of experience.
Panelists
Product Marketing leader who knows how to turn complex technology into stories that inspire and connect.
Technologist with 30+ years working with Enterprise Storage and Data Management, more recently working with global organizations on AI Infrastructure as well. Author of the Authoritative Core architectural design pattern.
Stephen is the President of the Tech Field Day business unit for The Futurum Group and focuses on AI, edge, and cloud