With the decision-making environment becoming more complicated in the enterprise world, artificial intelligence is accelerating the outcome-driven narrative, as optimal choices go beyond just having data.

As an AI, analytic and data management platform, SAS Viya makes this a reality by offering better business insights through a lightweight programming environment that involves integrating synthetic data, digital twins and large language models, according to Udo Sglavo (pictured, right), vice president of advanced analytics research and development at SAS Institute Inc.

“People were lacking the capability of writing code themselves … this is where we started 10 years ago to create what we now call the Viya Enterprise ecosystem,” Sglavo said. “We are not so much interested in the low-code, no-code environments; we just want to code away. I think this is why we released the Workbench. We have a broader strategy, the three pillars we talked about, digital twins, synthetic data and LLMs to solve business problems.”

Sglavo and Alice McClure (left), director of product marketing at SAS, spoke with theCUBE industry analysts John Furrier and Dave Vellante at SAS Explore, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how SAS is making AI usage as seamless as possible by taking the coding pain point away using platforms, such as SAS Viya. (* Disclosure below.)

AI is SAS tailwind

With AI being at the heart of SAS, the company has been working toward a smooth and easy application process. SAS has made this a reality through its Viya platform offerings, such as Workbench and AppFactory, which allow for a lightweight and accessible environment, according to McClure.

“We want to create a sandbox kind of mentality for developers to be able to be innovative and to be able to do their work in an easy way and also have access to the data in a very straightforward fashion,” she said.

By taking the computer science out of the data science, SAS seamlessly operationalizes the AI model for users who are not tech experts. For instance, SAS helped Cambridge University to easily match kidney donations through AI-powered image analytics, Sglavo pointed out.

“Cambridge University approached us with a desire to match kidney donations to patients who are waiting for a kidney transplant,” he said. “In the past, they had to look at pictures and basically try to understand, is this kidney a good match for a certain patient? Now we are using image analytics, AI to analyze those pictures and give recommendations to the practitioners. We can basically allow our users to focus on solving the business problem, and we take care of the computer science for them behind the scenes.”

With the AI wave hitting, the influx of models and automation should not be as overwhelming. As a result, AI model management is emerging as a game-changer, according to Sglavo.

“All of a sudden, you have an explosion of models, so how do you keep track of which models are still relevant, which are still giving you the answers, which you’re actually wondering about, and when should you replace those models?” he asked. “This is where model management is coming in big time.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of SAS Explore:

(* Disclosure: SAS Institute Inc. sponsored this segment of theCUBE. Neither SAS nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE