In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), the ability to manage and process various types of data is paramount to the quality of the insights produced by the models.
An optimal storage strategy needs to factor in the following things:
Pulling together a data architecture for widespread AI adoption in the enterprise is a non-trivial task. Therefore, it’s not surprising that many companies that procure GPU servers or access them through hyperscalers get stuck at the data management phase. IDC’s research indicates that data movement/management is one of the most common blockers for successful AI deployment.
With a unified and intelligent approach to infrastructure, NetApp enables AI teams to transcend the boundaries of siloed data regardless of how or where it is stored. Here are the specific benefits that make NetApp critical for AI workflows:
NetApp customers have enjoyed a unified, hybrid-multi-cloud experience for years. In fact, even though NetApp could not predict the explosion of generative AI over the last 12 months, we were busy building an intelligent data infrastructure engineered for data driven companies. It turns out that this framework is exactly what is needed for enterprises to leverage AI and generative AI for competitive advantage.
To learn more about what IDC has written on data architectures for AI workflows, read Unified Data Architectures Provide Needed Flexibility for AI Workflows. And find out more about NetApp executive perspectives on AI and generative AI.
Arun Gururajan is the Vice President of Research & Data Science at NetApp, overseeing AI/ML/Data Science initiatives across the company’s product range. Previously, he has served in various leadership roles across Meta and Microsoft, developing AI-powered products with broad and lasting adoption.