Designing and Building a Data Pipeline for Your AI Workflows

How to plan a data architecture for AI

Designing and Building a Data Pipeline for Your AI Workflows reportEnterprises 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

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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.


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