Even though the workloads are only a few years old, the typical file system used in AI and High Velocity Analytics was created decades ago. These file systems, while parallel in nature, were optimized for large file, high bandwidth environments,…
Blog Archives
New eBook – Selecting a File System for AI and High-Velocity Analytics Workloads
As Artificial Intelligence, Machine Learning and Deep Learning workloads go mainstream, organizations are struggling with how to develop a storage infrastructure to best meet the unique challenges of these workloads. AI/ML workloads typically include hundreds, if not thousands of servers…
WekaIO for AI and High-Velocity Analytics
Storage Switzerland has previously discussed the problems that legacy storage file systems have when it comes to serving modern workloads such as artificial intelligence (AI) and high-velocity analytics. We have also explored the qualities that a modern file system requires.…
Debunking AI and High Velocity Analytics Benchmark Results
Benchmarks are necessary when trying to understand the performance characteristics of a particular storage system in a particular environment. The problem is they are susceptible to manipulation by vendors to get the best marketing results. The Standard Performance Evaluation Corporation…
Designing a File-System for AI and High-Velocity Analytics
Our previous blog highlighted the challenges of supporting artificial intelligence (AI), machine learning (ML) and deep learning (DL) workloads with legacy file systems. Control node bottlenecks, inferior (or lack of) non-volatile memory express (NVMe) drivers, and inefficient capacity utilization are…
Why Legacy File Systems Can’t Keep up With AI and High-Velocity Analytics
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) workloads often start as skunk works projects within an organization. After the proof of concept and testing they move into production, which means storage performance and capacity demands for the…
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