NetApp > Case Studies > The AI integrated system “NetApp ONTAP AI” adopted to “AI_LAB,”

The AI integrated system “NetApp ONTAP AI” adopted to “AI_LAB,”

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 The AI integrated system “NetApp ONTAP AI” adopted to “AI_LAB,”  - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions
  • Business Operation
  • Data Science Services
The Challenge

In the last few years, opportunities to verify data and service integrations between the TSC system and the public cloud are growing exponentially, as companies take advantage of connecting to Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). There has also been a remarkable increase in the number of AI/deep learning verification projects.

The growing need for verification of hybrid cloud and AI/deep learning has driven CTC to create this new verification environment.

The integrated system NetApp ONTAP AI, jointly developed by NetApp and NVIDIA, was adopted for the AI-dedicated verification environment AI_LAB.


The Customer

ITOCHU Techno-Solutions

About The Customer

ITOCHU Techno-Solutions has built a large-scale AI verification environment using the NetApp ONTAP AI integrated AI system. The company is developing an image library using Docker containers and wants to realize an advanced AI verification environment that can quickly respond to various verification requests.

The Solution

Adoption of NetApp ONTAP AI, jointly developed by NetApp and NVIDIA

The NetApp ONTAP AI integrated system integrates the NVIDIA DGX-1 AI, incorporating eight Tesla V100s and the NetApp AFF A800 high-end all-flash array and forming a reference architecture that is optimized for AI/ deep learning applications.

NetApp's AI integrated system “NetApp ONTAP AI” has been adopted as the core system. Having conducted various AI/deep learning verification tests that include cloud integration, it is now making a significant contribution to the utilization of AI for business by client companies.

Operational Impact
  • [Product Improvement - User Experience]

    Integrate to public clouds easily

  • [Financial Growth - Market Opportunities]

    Achieving better business outcomes using AI/deep learning

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