Designing and Building a Data Pipeline for Your AI Workflows
How to plan a data architecture for AI
Enterprises are eager to take advantage of artificial intelligence (AI) technologies, such as deep learning (DL), to introduce new services and enhance insights from company data. As data science teams move past proof of concept to operationalize deep learning, they must focus on creating a data architecture that eliminates bottlenecks and facilitates faster model iteration.
Designing a data architecture involves thinking holistically about the data pipeline: from data ingest and edge analytics to data preparation, training in the core data center, and archiving in the cloud. It is critical to understand the performance requirements, datasets, and data services needed.
Overcome AI infrastructure challenges
Read the white paper to learn how NetApp can help you evolve your AI architecture over time:
- Build a data pipeline for deep-learning workflows–for today and tomorrow
- Future-proof investments in your AI infrastructure
- Lead to faster deployment times
- Maximize competitive differentiation
Learn more about NetApp and Nvidia’s partnership
Check out the “Accelerate Your Journey to AI with NetApp and NVIDIA” video and learn how to simplify, accelerate and integrate your data pipeline for deep learning with NetApp ONTAP AI, powered by NVIDIA DGX supercomputers and NetApp cloud-connected, all-flash storage.
If you do not receive the email from us within the next 30 minutes, please check your spam, junk mail filter, or ad blocker. If you didn't get our email at all, please contact us at ng-EmailSupport@netapp.com and we will resolve the issue promptly.