Nutanix Inc. is strengthening its collaboration with Amazon Web Services Inc. by running its Enterprise AI solution on top of Amazon’s Elastic Kubernetes Service, or EKS.

The partnership between the two companies came about naturally, according to Debojyoti Dutta (pictured), vice president of engineering at Nutanix. Businesses are looking for consistency across their AI workloads, and Nutanix Enterprise AI was designed to provide a simple, secure experience for business users, he explained.

“One of the key components of that generative AI application development lifecycle, whether you do agents or rag, is inference,” Dutta said. “We are focused in Nutanix Enterprise AI on how to do inference really well for the enterprise. The way the product works is very simple. We simplify the entire lifecycle of inference for our customer. A customer can go and choose any model from Hugging Face or from the Nvidia catalog and then deploy the model very easily with a couple of button clicks.”

Dutta spoke with theCUBE Research’s Rob Strechay for theCUBE’s “Cloud AWS re:Invent Coverage,” during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the value of running Enterprise AI on EKS and Nutanix’s work on inference.

Combining innovative AI solutions with Amazon EKS

Nutanix Enterprise AI is part of the company’s “GPT in a box” solution, with a model endpoint that runs an application user interface similar to OpenAI and a system of access tokens that reduce costs for users. Nutanix has extended the entire offering onto Amazon EKS.

“Many of our customers are also customers of AWS EKS, and they’re using EKS for running all their workloads,” Dutta explained. “You want to typically co-locate your AI where your other workloads are, where your data is … most of our customers want a common operating model; they want consistency. Most of our customers, including ourselves, we are hybrid in terms of where our computation is, where our data is and where the AI is going to run.”

The user interface and tooling needs to be consistent whether customers are running AI on-premises and in the cloud, according to Dutta. Nutanix has also used AI internally, creating a support bot that enhances customer interactions and incorporating machine learning to help software engineers become more efficient.

“We are finding out that there’s so many use cases … we have to prioritize them and make sure that these are of high quality, because one of the biggest challenges in building internal use cases or external use cases is data quality and the ability to actually have good outcomes and results from these data science experiences,” Dutta said.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s “Cloud AWS re:Invent Coverage”:

Photo: SiliconANGLE