Infrastructure requirements for AI adoption at scale
Artificial Intelligence Series
Session 1 of 4
Abstract
As enterprises increase their use of AI, they face many challenges, including workload scalability and data availability. Now you can fully realize the promise of ML and DL by simplifying, accelerating, and integrating your data pipeline with NetApp and NVIDIA. Learn how the NetApp Data Science toolkit enables data scientists and data engineers to provision, destroy, clone, and snapshot data volumes in seconds. Hear how the NetApp AI portfolio delivers efficient and scalable performance for the most demanding ML/DL workloads.
Key takeaways
- Improve your models—we help you create an end-to-end pipeline with 6-9x faster model training
- Enable seamless hybrid cloud AI training and inferencing
- Bring your data where your data scientist resides 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 sessions
Check out other sessions 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.
Ray White
Pursuit Business Development Manager
Dan Holmay
Global AI Pursuit Business Development Manager
Tony Paikeday
NVIDIA l Sr Director Product Marketing l AI Systems
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.
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