Custom model training is bringing enterprise AI from experimentation to production
As artificial intelligence moves from proof of concept into enterprise production, custom model training on governed data is emerging as the critical unlock for organizations that need domain-specific accuracy without sacrificing security or control.
The shift is pressing enterprise platforms to rethink how they deliver model training infrastructure. Rather than forcing customers to move sensitive data to external GPU clouds, the winning approach keeps training inside the governed environment where the data already lives, according to Dwarak Rajagopal (pictured, left), vice president of AI engineering and research at Snowflake Inc.
“We are extending [Cortex Training] to actually train custom models in a safe, governed environment,” Rajagopal said. “What we hear from a lot of our customers is that customizing models for their unique use cases and their unique enterprise data is super critical. We handle the infrastructure, the distributed systems, the GPU sourcing — all of that.”
Rajagopal and Spiros Xanthos (right), founder and CEO of Resolve AI Inc., spoke with theCUBE’s Dave Vellante and Rebecca Knight at Snowflake Summit 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Cortex Training enables governed custom model training and why the combination of frontier and specialized models is becoming the standard for production AI. (* Disclosure below.)
