Lessons learned for AI data management at scale

Artificial Intelligence Series

Session 2 of 2

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Data scientists demand a best-in-class workbench experience, with efficient data pipelines and scalable compute performance. Converged infrastructure paired with MLOps can address the most common AI workflow pain points, streamlining the flow of data reliably and accelerating analytics, training, and inference. With NetApp and NVIDIA, build an integrated data pipeline that spans from edge to core to cloud. ONTAP AI leverages your data fabric to unify data management across the data pipeline with a single platform. Use the same tools to securely control and protect your data in flight, in use, or at rest, and meet compliance requirements with confidence. Hear how the NetApp AI portfolio, including the NetApp DataOps Toolkit and AI Control Plane delivers efficient and scalable performance for the most demanding ML/DL workloads.

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Key takeaways

  • Enable seamless hybrid cloud AI training and inferencing – move your data where your data scientists reside and let them seamlessly collaborate.
  • Get a faster ROI from your AI investments faster – results by eliminating days and months of configuration, support, and other non-data science essential tasks.
  • Future proof – protect your investment, start small with a POC and scale efficiently and limitless.
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Other session

Check out the other session in this series:

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Speaker bio by region

Meet the specialists ready to help simplify your data challenges. Expect to dive into real-world, industry-specific questions—they’re experts who love to talk shop.


Dave Arnette 

TME, NetApp  

Dave’s LinkedIn 

Mike Oglesby  

TME, NetApp 

Mike’s LinkedIn 

Sathish Thyagarajan 

TME, NetApp  

Sathish’s LinkedIn 

Rick Huang 

TME, NetApp 

Rick’s LinkedIn 

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