AI Production Optimization with Unified Data Stacks

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

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From time-sensitive workloads like manufacturing failure prediction or real-time fraud detection in retail and e-commerce, to the increased agility required in a crowded marketplace, time to implementation is critical. for businesses that rely on artificial intelligence, machine learning, and data analytics. But IT leaders have found it very difficult to move from proof of concept to production AI at scale.


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The obstacles to production AI vary, says Erik Grundstrom, director of 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 obstacles or components are increasingly common. Then there’s the human part of the equation: whether the leadership within a business or organization understands the model well enough to trust the outcome and support the IT team’s AI initiatives.

“You want to implement as quickly as possible,” Grundstrom says. “The best way to address that would be to continually optimize, 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 to move away from a data stack full of disparate hardware and software and implement an end-to-end production AI platform, he adds. You’ll be turning to a partner that has the tools, technologies, and scalable and secure infrastructure needed to support your business use cases.

End-to-end platforms, often provided by the big cloud players, incorporate a wide range of essential features. Look for a partner that offers predictive analytics to help extract insights from data and support for hybrid and multiclouds. These platforms offer a scalable and secure infrastructure, so they can handle any size project thrown at them, as well as robust data governance and features for data management, discovery, and privacy.

For example, Supermicro, in partnership with NVIDIA, offers a selection of NVIDIA-certified systems with the new NVIDIA H100 Tensor Core GPUs, within the NVIDIA AI Enterprise platform. They are capable of handling everything from the needs of small businesses to massive, unified AI training clusters. And they offer up to nine times the training performance of the previous generation for challenging AI models, reducing a week of training time to 20 hours.

NVIDIA AI Enterprise itself is a comprehensive, secure, cloud-native AI software suite, including AI solution workflows, frameworks, pre-trained models, and infrastructure optimization—in the cloud, in the data center, and on the perimeter.

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

Migration challenges

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

And then, just when you think you might be out of the woods, another nine innings are waiting for you, Grundstrom says.

“Until the move is made, you can’t predict how it will perform, or ensure that it will perform adequately, and there’s no guarantee that there’s a solution on the other side,” he explains. “To overcome these integration challenges, there is 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.”

Take advantage of critical experience

“Build a strong team: Make sure you have the right people,” says Grundstrom. “Once your team agrees on a business model, take an approach that allows you to have a quick turnaround time to prototype, test, and refine your model.”

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

To learn more about how businesses can rid themselves of the muddled data pile, 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 the current differentiator
  • Challenges in deploying AI/AI production at scale
  • Why disparate hardware and software solutions create problems
  • New Innovations in Complete End-to-End Production AI Solutions
  • A look under the hood at the NVIDIA AI Enterprise platform


  • ana hechtSenior Director, Product Marketing, Enterprise Computing Group, NVIDIA
  • Erik GrundstromDirector, FAE, Supermicro
  • joe maglitaSenior Director and Publisher, VentureBeat (moderator)

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