Optimizing AI production with unified data stacks

Presented by Supermicro/NVIDIA

Rapid deployment and high performance are critical for enterprise AI, machine learning, and data analytics workloads. At this VB Spotlight event, you’ll learn why an end-to-end AI platform is critical to providing the capabilities, tools, and support to create AI value for business.

Watch for free on demand here.

From time-critical workloads such as predicting manufacturing failures or real-time fraud detection in retail and e-commerce, to the increased flexibility required in a crowded marketplace, time to deployment is critical for businesses that rely on AI, machine training and data analysis. But CIOs have struggled to move from proof-of-concept to large-scale AI production.

According to Eric Grundstrom, Director of FAE at Supermicro, the obstacles to creating artificial intelligence are different.

There is the quality of the data, the complexity of the model, how well the model can scale to meet growing demand, and whether the model can be integrated into existing systems. Regulatory hurdles or components are becoming more common. Then there is the human side of the equation: does the management of the company or organization understand the model well enough to trust the result and support the AI ​​initiatives of the IT team.

“You want to deploy as quickly as possible,” says Grundström. “The best way to solve this problem is to constantly optimize, constantly test, constantly work to improve the quality of your data, and find a way to reach consensus.”

The power of a single platform

The foundation of this consensus is moving away from the data stack full of disparate hardware and software and adopting an end-to-end AI manufacturing platform, he adds. You’ll connect with a partner that has the tools, technology, and scalable and secure infrastructure you need to support your business use cases.

Comprehensive platforms, often supplied by major cloud computing players, include a wide range of core features. Look for a partner that offers predictive analytics to help you extract insights from your data, as well as support for hybrid and multi-cloud environments. These platforms offer a scalable and secure infrastructure so they can handle projects of any size, as well as robust data management and features for data management, discovery, and privacy.

For example, Supermicro has partnered with NVIDIA to offer a range of NVIDIA-certified systems with the new NVIDIA H100 Tensor Core GPUs powered by the NVIDIA AI Enterprise platform. They are capable of handling everything from the needs of small businesses to large unified AI training clusters. In addition, they provide nine times better training performance than the previous generation for complex AI models, reducing the training week to 20 hours.

NVIDIA AI Enterprise itself is a comprehensive, secure, cloud-based set of AI software that includes AI solution workflows, frameworks, pre-trained models, and infrastructure optimization across the cloud, data center, and edge.

But when moving to a unified platform, enterprises face a number of serious obstacles.

Migration issues

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

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

“Until the move is done, there is no way to predict how it will perform or guarantee that you will achieve adequate performance, and there is no guarantee that there will be a fix on the other side,” he explains. “To solve these integration problems, there is always outside help in the form of consultants and partners, but it’s best to have the right people inside the company.”

Using Critical Experience

“Build a strong team – make sure you have the right people,” Grundström says. “Once your team has agreed on a business model, adopt an approach that allows you to quickly prototype, test, and refine your model.”

Once you do this, you should have a good idea of ​​how you need to scale initially. This is where companies like Supermicro come in, who can keep testing until the customer finds the right platform, and then tweak performance until AI becomes a reality.

To learn more about how businesses can get rid of a messy data stack, implement a complete AI solution, increase speed, power, innovation and more, don’t miss this VB Spotlight event!

Watch on demand now!


  • Why time to AI business value is a differentiator today
  • Challenges of Deploying AI/AI Production at Scale
  • Why disparate hardware and software solutions create problems
  • New innovations in end-to-end artificial intelligence solutions for manufacturing
  • An inside look at the NVIDIA AI Enterprise platform


  • Ann HechtSenior Director of Product Marketing, Enterprise Computing Group, NVIDIA
  • Eric Grundstromdirector, FAE, Supermicro
  • Joe Maglittasenior director and editor of VentureBeat (moderator)

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