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Unified data architectures for AI workflows

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Arunkumar Gururajan
Arun Gururajan
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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.

Optimizing the storage strategy

An optimal storage strategy needs to factor in the following things:

  • The various data types that enterprises use (structured data like databases and spreadsheets and unstructured data like emails, images, audio, video and documents).
  • The location where these data types reside, (on-premises and/or within one or multiple public clouds or SaaS providers that are spread across multiple geographic regions).
  • The types of storage architectures such as file storage (data is accessed and managed as files), block storage (data is stored as blocks that enable efficient, low-latency operations), and object storage (data is managed as objects, each containing data, metadata, and unique ID, making it highly scalable and suitable for unstructured data).

Overcoming blocks to AI deployment

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:

  • Data movement: Especially in today’s multimodal AI, AI and ML workflows often involve moving large datasets across different stages of processing. This movement is facilitated by a unified storage architecture, which provides the right type of storage for each stage, optimizing for speed, accessibility, or durability as needed. This approach is essential for operationalizing AI, because it requires massive amounts of data to move unhindered through the inferencing pipeline.
  • Data management: The diverse and multimodal nature of AI datasets, which include images, videos, sensor data, and more, requires a flexible approach to data management that is provided through NetApp’s intelligent infrastructure. A NetApp storage system can store these diverse datasets effectively, so that they are readily accessible for complex AI tasks. For example, in healthcare applications, medical imaging workflows benefit from block storage for high-performance access to imaging data, while patient records and other unstructured data can be stored as objects with rich metadata for easy retrieval and analysis.
  • Data governance: Enterprises must make sure that the right data is leveraged with the appropriate access controls while complying with internal policies and local regulations, all while keeping the organization’s intellectual property secure and protected. With NetApp’s data management capabilities data governance is built in, by design.

Building an intelligent data infrastructure

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.

Learn more

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

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.

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