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Pure Storage adds pace to AI adoption

Pure Storage adds pace to AI adoption


Pure Storage, has announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale.Built on Pure’s industry-leading file and object system, FlashBlade, and its joint AI-Ready Infrastructure (AIRI) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business.

“Enterprise organizations that have existed and done business one way for decades now find themselves working hard to build a business for the future. To truly compete going forward will require large-scale, multi-phase AI initiatives, and Pure has innovated with that particular set of challenges in mind, said Amy Fowler, VP of Strategy and Solutions for FlashBlade at Pure Storage.

Organizations today are stuck with a siloed, traditional analytics infrastructure. AI Data Hub extends traditional analytics and provides more performance and security at a lower cost. Data scientists can expect major improvements in productivity, faster time-to-market, and more informed data-driven models.

“With AIRI powered by NVIDIA DGX systems and Pure Storage Flashblade, NVIDIA and Pure have been making it faster, easier and more cost-effective for every enterprise to deploy and manage AI at scale,” said Charlie Boyle, vice president and general manager of DGX systems at NVIDIA. “The AI Data Hub increases the value of our joint solutions by accelerating data science workflows, powered by NVIDIA CUDA-X AI software and DGX systems for faster iteration, with greater developer productivity and accelerated time-to-insight.

From cleansing and tagging, through modeling, training and production, AI Data Hub streamlines deployment for an entire AI pipeline instead of a single phase, and eliminates risk of failure when scaling from proof-of-concept to production. AI Data Hub is an end-to-end solution, from ingestion to inference, orchestrated by Kubernetes. It has three stages:

  • Customers can design and explore data faster with RAPIDS and PureTools.
  • Customers will experience faster training and model development with AIRI and FlashStack for AI.
  • Customers benefit from faster model deployment and inference with T4.