Streamline AI production with unified data stacks
Presented by Supermicro/NVIDIA
Rapid deployment and high performance are essential 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 to providing the power, tools, and support needed to create business value in AI.
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From time-sensitive workloads like fault prediction in manufacturing or real-time fraud detection in retail and e-commerce, to the increased agility required in a crowded marketplace, time to deployment is crucial for businesses that rely on AI, ML and data analytics. But IT managers have found it notoriously difficult to move from proof-of-concept to full-scale production AI.
The barriers to producing AI vary, says Erik Grundstrom, director, FAE, at Supermicro.
There is the quality of the data, the complexity of the model, the capacity of the model to evolve in the face of increasing demand, and the possibility of integrating the model into existing systems. Regulatory barriers 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 deploy as quickly as possible,” says Grundstrom. “The best way to solve this problem would be to constantly streamline, constantly test, constantly work to improve the quality of your data, and find a way to reach consensus.”
The power of a unified platform
The basis of this consensus is to move away from a data stack filled with disparate hardware and software and implement an end-to-end production AI platform, he adds. You’ll use a partner that has the tools, technology, and scalable and secure infrastructure to support business use cases.
End-to-end platforms, often provided by large cloud players, integrate a wide range of essential features. Find a partner that offers predictive analytics to help extract insights from data and support hybrid and multi-cloud environments. These platforms offer a scalable and secure infrastructure, so they can handle projects of any size, along with 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 small business needs to massive, unified AI training clusters. And they deliver 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 native, secure, end-to-end AI software suite, including AI solution workflows, frameworks, pre-trained models, and infrastructure optimization, in the cloud, in the data center and at the edge.
But when moving to a unified platform, businesses face significant hurdles.
The technical complexity of migrating to a unified platform is the first hurdle, and it can be significant without an expert in place. Mapping data from multiple systems onto a unified platform requires significant expertise and knowledge, not only of the data and its structures, but also of the relationships between the 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 when you think you might be out of the woods, you’re ready for another nine innings, Grundstrom says.
“Until the move is complete, there’s no way to predict its performance or ensure you’ll get adequate performance, and there’s no guarantee there’s a solution on the other side,” he says. -he. “To overcome these integration challenges, there is always outside help in the form of consultants and partners, but the best thing is to have the people you need in-house.”
Leverage critical expertise
“Build a strong team – make sure you have the right people in place,” says Grundstrom. “Once your team has agreed on a business model, adopt an approach that allows you to have a quick turnaround time for prototyping, testing, and refining your model.”
Once you have that, 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 the AI of production becomes a reality.
To learn more about how businesses can ditch the muddled data stack, embrace an end-to-end AI solution, unlock speed, power, innovation and more, don’t miss this VB event Spotlight!
- Why the time of AI business value is today’s differentiator
- Challenges of deploying large-scale AI/AI production
- 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 HechtSr. Director, Product Marketing, Enterprise Computing Group, NVIDIA
- Eric GrundstromDirector, FAE, Supermicro
- Joe MaglittaSenior Director and Publisher, VentureBeat (moderator)
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Streamline AI production with unified data stacks