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Streamlining AI production with unified data stacks

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 platforms, often delivered by the big cloud players, incorporate a broad array of essential features. Look for a partner offering predictive analytics to help extract insights from data, and support for hybrid and multi-cloud. These platforms offer scalable and secure infrastructure, so they can handle any size project thrown at it, as well as robust data governance and features for data management, discovery and privacy.

For instance, Supermicro, partnering with NVIDIA, offers a selection of NVIDIA-Certified systems with the new NVIDIA H100 Tensor Core GPUs, inside the NVIDIA AI Enterprise platform. They’re capable of handling everything from the needs of small enterprises to massive, unified AI training clusters. And they deliver up to nine times the training performance of the previous generation for challenging AI models, cutting a week of training time into 20 hours.

NVIDIA AI Enterprise itself is an end-to-end, secure, cloud-native suite of AI software, including AI solution workflows, frameworks, pretrained models and infrastructure optimization, in the cloud, in the data center and at the edge.

But when making the move to a unified platform, enterprises face some significant hurdles.

Migration challenges

The technical complexity of migration to a unified platform is the first barrier, and it can be a big one, without an expert in place. Mapping data from multiple systems to a unified platform requires significant expertise and knowledge, not only of the data and its structures, but about the relationships between different data sources. Application integration requires understanding the relationships your applications have with one another, and how to maintain those relationships when integrating your applications from separate systems into a single system.

And then when you think you might be out of the woods, you’re in for a whole other nine innings, Grundstrom says.

“Until the move is done, there’s no predicting how it will perform, or ensure you’ll achieve adequate performance, and there’s no guarantee that there’s a fix on the other side,” he explains. “To overcome these integration challenges, there’s always outside help in the form of consultants and partners, but the best thing to do is to have the people you need in-house.”

Tapping critical expertise

“Build a strong team — make sure you have the right people in place,” Grundstrom says. “Once your team agrees on a business model, adopt an approach that allows you to have a quick turnaround time of prototyping, testing and refining your model.”

Once you have that down, you should have a good idea of how you’re going to need to scale initially. That’s where companies like Supermicro come in, able to keep testing until the customer finds the right platform, and from there, tweak performance until production AI becomes a reality.

To learn more about how enterprises can ditch the jumbled data stack, adopt an end-to-end AI solution, unlock speed, power, innovation, and more, don’t miss this VB Spotlight event!

Watch on-demand now!


  • Why time to AI business value is today’s differentiator
  • Challenges in deploying AI production/AI at scale
  • Why disparate hardware and software solutions create problems
  • New innovations in complete end-to-end production AI solutions
  • An under-the-hood look at the NVIDIA AI Enterprise platform


  • Anne Hecht, Sr. Director, Product Marketing, Enterprise Computing Group, NVIDIA
  • Erik Grundstrom, Director, FAE, Supermicro
  • Joe Maglitta, Senior Director & Editor, VentureBeat (moderator)

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

Author: VB Staff
Source: Venturebeat

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