{"id":616623,"date":"2023-03-11T08:49:38","date_gmt":"2023-03-11T14:49:38","guid":{"rendered":"https:\/\/news.sellorbuyhomefast.com\/index.php\/2023\/03\/11\/accelerating-ai-deployment-and-scale-with-a-transformative-end-to-end-ai-platform\/"},"modified":"2023-03-11T08:49:38","modified_gmt":"2023-03-11T14:49:38","slug":"accelerating-ai-deployment-and-scale-with-a-transformative-end-to-end-ai-platform","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2023\/03\/11\/accelerating-ai-deployment-and-scale-with-a-transformative-end-to-end-ai-platform\/","title":{"rendered":"Accelerating AI deployment and scale with a transformative end-to-end AI platform"},"content":{"rendered":"
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Image Credit: Adobe Stock<\/em><\/p>\n<\/div><\/div>\n<\/p><\/div>\n

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Presented by Supermicro\/NVIDIA<\/em><\/p>\n

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AI delivers business value and a competitive advantage for enterprise, but there\u2019s one obstacle:<\/strong> graduating from proof of concept to production AI at scale. In this VB Spotlight event, learn how an end-to-end AI platform helps deliver strategic projects and business value fast.<\/strong><\/p>\n

Watch free on-demand!<\/a><\/strong><\/p>\n

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\u201cAI is as transformative as the internet to the structure of business, how business is being done and its impact,\u201d says Anne Hecht, senior director, product marketing, enterprise computing\u00a0group at NVIDIA. \u201cEvery business and department is starting to use AI and finding opportunities to operationalize, be more efficient and develop more intimate relationships with their customers.\u201d<\/p>\n

Consumers are interacting with these AI products every day, from the recommendation engines developed by marketing departments to the intelligent virtual assistants, which enable customers to get results faster, to route optimization for logistics departments (and faster pizza delivery for us). It\u2019s a transformative technology already, but generative AI and applications like ChatGPT are shaking up the way business is done. Enterprises are looking for ways to unlock the potential of AI, and realize cost savings, operational benefits and new business models.<\/p>\n

\u201cDespite all these opportunities, we\u2019re finding that enterprises are challenged to move these use cases into full production,\u201d Hecht says. \u201cThere\u2019s tremendous potential, and yet only \u2014 maybe a third of enterprises are in full production with AI<\/a> right now.\u201d<\/p>\n

The challenges of deploying AI at scale<\/strong><\/h2>\n

The challenges range from the technical to the human, says Erik Grundstrom, director, FAE at Supermicro. Cost is always number one, of course. But on the technology side, there\u2019s the technical complexity of migrating disparate systems into a unified platform. Then there\u2019s mapping data from multiple systems to a unified platform, which requires deep understanding of the data structure and relationships between the data.<\/p>\n

The application environment often requires multiple teams, each with their own expertise, working together to create a singular platform \u2014 and on top of that, ensure the data is still reliable and the applications remain high performing.<\/p>\n

\u201cPulling that team together is probably the biggest challenge today,\u201d Grundstrom says. \u201cDisparate groups within a company are all working on their own models and projects, in their own departments.\u201d<\/p>\n

The support team\u2019s environment used to develop a chat bot is very different from the environment and the tools being used by the team doing the recommendation engine, and there\u2019s no unification of infrastructure and resources across all these environments. When everyone\u2019s just doing their own thing, it turns into the wild west.<\/p>\n

\u201cCreating a unified structure presents a lot of new challenges at the enterprise level,\u201d Grundstrom says. \u201cBut companies that are making that happen are benefiting the most out of predictive analytics and getting the best quality information from their AI at scale.\u201d<\/p>\n

The other key issue that makes AI production complicated for enterprises is that it\u2019s much different than a standard enterprise application, Hecht adds. You don\u2019t build it, deploy it and come back and do an update 12 months later. An AI application is continuously run and trained with new data for additional inferencing, to keep it current, make it smarter and ensure it adapts to evolving circumstances. On top of that, you need to consistently ensure the quality and integrity of your data.\u00a0<\/p>\n

\u201cIt takes most enterprises, on average, about seven to seven and a half months to develop and train a model<\/a>,\u201d Hecht says. \u201cOften they\u2019re leveraging a pre-trained model. And then moving it into production. Then they\u2019re still dealing with the fact that almost half of those never make it to production. If we can reduce that time, that\u2019s very powerful for our customers.\u201d<\/p>\n

Accelerating the AI pipeline<\/strong><\/h2>\n

Enterprises early in their journey commonly have developers and teams building out their own infrastructure, leveraging a cloud instance, or developing on local workstations or PCs. They\u2019re using open-source frameworks and pre-trained models, to do their development work. Those tools can be a great place to start, but where they fail enterprises is their incompatibility. And thus, applications developed in these highly customized shadow IT environments often can\u2019t be deployed into the data center, or end up patched in, rather than assimilated, and it becomes incredibly difficult to scale. AI production becomes a hassle instead of a win.<\/p>\n

To solve this, the AI pipeline must be optimized to accelerate every step and get to market with an application within days as opposed to months. Adding acceleration cuts down a lot of the time it takes to train and process the data as well, which means cutting costs, because you don\u2019t need as much infrastructure. An end-to-end production AI platform, which comes along with a partner and tools, technologies and scalable and secure infrastructure, is essential.<\/p>\n

The companies that are becoming successful are driving this from a strategic standpoint. They\u2019re taking the time to develop the full business strategy, and approaching AI as a center of excellence, putting together the governance, processes, people and teams. They are making the infrastructure investments, while including security practices, privacy practices and data management practices to make AI core to their business.<\/p>\n

\u201cIf you start from that standpoint, it\u2019ll naturally reveal what infrastructure you need and which partners you want to work with, so that you build out a comprehensive and streamlined AI infrastructure for your business,\u201d Hecht says. \u201cSomething that\u2019s flexible, that can address any AI workflow, any AI opportunity that might present to your organization and to your business.\u201d<\/p>\n

To learn more about the infrastructure and partners that are foundational to successful production AI, a deep dive into the power of NVIDIA AI Enterprise and more, don\u2019t miss this VB Spotlight!<\/p>\n

Watch on-demand now!<\/a><\/strong><\/p>\n

Agenda:<\/strong><\/p>\n