Lessons learned for AI data management at scale
Artificial Intelligence Series
Session 2 of 2
Abstract
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.
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.
Other session
Check out the other session in this series:
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.
AMER
Dave Arnette
TME, NetApp
Mike Oglesby
TME, NetApp
Sathish Thyagarajan
TME, NetApp
Rick Huang
TME, NetApp
EBC Solution Series
Learn, collaborate, and engage with NetApp experts and industry peers on topics ranging from high-level cloud services overviews to technical deep dives, such as Ansible workshops.
