So many conversations about implementing AI in the enterprise revolve around hardware performance, time to result, and overall business objectives but there is a critical piece in the middle that can determine the success or failure of experimental AI—the development platforms.

Building models for training or deploying efficient, accuracy inference is hard enough, but managing the complex assortment of frameworks, algorithms, and optimization tools is another story entirely. Developers want the flexibility to pick the right tools without being locked in and more importantly, want to add and subtract elements without restructuring an entire strategy.

In this webcast, we’ll dive into that very complexity in use case context, understanding why AI experiments fail—how they can thrive and shift into production.

At the heart of this conversation with experts from Red Hat and Intel, we’ll talk about building on a RedHat OpenShift base and using tools that value AI productivity and flexibility, including OpenVINO and OneAPI AI Kit to seamlessly bounce between experimentation and production with cutting-edge tools that optimize, streamline, and focus AI initiatives at any scale.

To get a handle on how these platforms intersect for high performance enterprise AI, join Will McGrath Product Marketing Manager, Cloud Data Services and Karl Eklund Principal Architect, OpenShift Data Science, Red Hat and Rachel Oberman AI Technical Consulting Engineer and Ryan Loney OpenVINO Product Manager, Intel Corporation in this wide-ranging chat, moderated by Nicole Hemsoth.