Presented by Supermicro/NVIDIA

Fast time to deployment and high performance are critical for AI, ML and data analytics workloads in an enterprise. In this VB Spotlight event, learn why an end-to-end AI platform is crucial in delivering the power, tools and support to create AI business value.

Watch free on-demand here.

From time-sensitive workloads, like fault prediction in manufacturing or real-time fraud detection in retail and ecommerce, to the increased agility required in a crowded market, time to deployment is crucial for enterprises that rely on AI, ML and data analytics. But IT leaders have found it notoriously difficult to graduate from proof of concept to production AI at scale.

The roadblocks to production AI vary, says Erik Grundstrom, director, FAE, at Supermicro.

There’s the quality of the data, the complexity of the model, how well the model can scale under increasing demand, and whether the model can be integrated into existing systems. Regulatory hurdles or components are increasingly common. Then there’s the human part of the equation: whether leadership within a company or organization understands the model well enough to trust the result and back the IT team’s AI initiatives.

“You want to deploy as quickly as possible,” Grundstrom says. “The best way to tackle that would be to continually streamline, continually test, continually work to improve the quality of your data, and find a way to reach consensus.”

The power of a unified platform

The foundation of that consensus is moving away from a data stack full of disparate hardware and software, and implementing an end-to-end production AI platform, he adds. You’ll be tapping a partner that has the tools, technologies and scalable and secure infrastructure required to support business use cases.

End-to-end pla...