Prepare Your Storage for the Impact of Artificial Intelligence Workloads

Apr 04 2022

NGD Systems

According to polling results conducted in January 2020 by the AI Research Circle, 85% of infrastructure and operations (I&O) leaders are looking to use artificial intelligence (AI) in their infrastructures during next two years. AI workloads are diverse and some are fundamentally different from any other workload the organization may have run in the past. Although interest in leveraging AI applications is on the rise, I&O leaders are often unprepared to address storage requirements and data management challenges for growing datasets of large-scale machine learning (ML) deployment. 

Infrastructure and operations leaders choosing infrastructure for AI workloads involving machine learning and deep learning should assess the workloads’ requirements. Here, we analyze an AI workloads’ impact on data infrastructure, and outline best practices for storage selection and implementation.

  • The storage requirements of machine learning workloads, versus deep-learning workloads, demand complementary storage technologies.
  • Unique requirements for different stages of the artificial intelligence/ML workload require that infrastructure and operations leaders reevaluate their approaches to storage and data management, and embrace new practices and deployment models.
  • The vendor ecosystem supporting ML workloads is rapidly evolving, affecting infrastructure and operations leaders’ vendor selections.

 

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    Computational Storage Drives Successful Training and Inference at the Data Source

    There are many ways to look at the future of Artificial Intelligence workloads, Computational Storage is part of that Solution for the future of Data Management:

    Latency is imperative – Our Computational Storage products allow for this to be managed more effectively than any existing architecture, especially in edge and core constrained environments where data is growing rapidly.

    Data growth is clogging bandwidth – With Computational Storage doing localized processing of ingested data, the amount of bandwidth needed to realize value from data is reduced significantly.

    Computational Storage can help minimize the attack surface by ensuring that your data is never exposed beyond the point of ingest, ensuring valuable data has security and self-protection built-in.

    With the growth of data at the edge, endpoint and core, utilizing new technologies like Computational Storage can improve AI significantly.

    The Newport Platform was designed to address these and more issues that have been overlooked for generations of storage architecture.

    GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission.

    This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from NGD Systems.

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