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.…
15 Minute Webinar – Finding the Right File System for AI and ML Workloads
Artificial Intelligence, Machine Learning and High Velocity Analytic workloads are going mainstream. Enterprises of all types and sizes want to seize the opportunity their data presents. As these workloads move from development to production, organizations face a significant challenge with…
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…
SlideShare: Three Reasons Why NAS is No Good for AI and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no…
Tagged with: AI, Artificial Intelligence, Cloud, HPC, Machine Learning, ML, NAS, WekaIO
Posted in Slideshare
Posted in Slideshare
How is Storage IO Different for AI vs. HPC?
Most Artificial Intelligence (AI) projects start off as a skunkworks project that when ready for production is tossed over the wall for IT to handle. In many cases, IT tries to store the AI workload on an existing network attached…
Tagged with: AI, Artificial Intelligence, Cloud, HPC, Machine Learning, Metadata, ML, NAS, WekaIO
Posted in Blog
Posted in Blog
AI Needs an NVMe-Optimized File System
Analytics is evolving from big data, machine learning to artificial intelligence. Machine learning is the analysis of data at rest, artificial intelligence (AI) is the analysis of data in real-time. Machine learning is predictive; AI is cognitive. The requirements of…
Tagged with: AI, Artificial Intelligence, Cloud, HPC, HPE, Machine Learning, ML, NAS, WekaIO
Posted in Blog
Posted in Blog
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