AI prototypes fail in production not from model or data problems, but from five infrastructure gaps that IT teams have solved for CPU workloads but not yet addressed for GPU-based AI. Learn what AI production infrastructure requires and how virtual data centers close the gap.
Artificial Intelligence (AI) at scale raises the bar for storage infrastructure in terms of capacity and performance. It is not uncommon for an AI or machine learning (ML) environment to expect growth to dozens if not hundreds of terabytes of…
In our last blog we covered the challenges that AI at scale creates for storage infrastructures. To support the coming wave of AI applications, storage infrastructures need to deliver a tremendous amount of storage capacity with the ability to retain…
Artificial Intelligence (AI) is in its infancy and the requirements it places on the storage architectures that support these workloads are not widely understood. As a result, an organization starting an AI initiative can initially get away with using an…
Artificial Intelligence (AI) is a broad term that can apply to various computing tasks, including machine learning, deep learning, and big data analytics. Many AI projects are in a proof of concept stage, but CIOs and IT Managers need to…
More Proactive Infrastructure Operations and Cost-Effective, Granular Application Performance Acceleration Graphics processing units (GPUs), software-defined data center architectures, and more intelligent infrastructure management are important technologies that all stand to help IT professionals meet modern business requirements. These include ever-faster…
Composable infrastructure, whereby resources are disaggregated and may be re-composed on the fly, stands to serve a number of key, future-forward IT infrastructure requirements. Resources may be added and then returned for use by a different application on the fly.…
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