CAMPBELL, Calif., December 2, 2021 – AI data platform vendor WekaIO (Weka) today announced the availability of its latest version of WekaFS, the fastest storage for data-intensive applications that provides users with enhanced capabilities for cloud deployments and provides new capabilities for both cloud native and virtualized applications on premises. Weka’s data platform, built on WekaFS, […]
WekaIO and Rancher Labs in Alliance to Simplify Kubernetes Deployments
CAMPBELL, Calif. – December 16, 2020 – WekaIO (Weka), specializing in high-performance and scalable NVMe-optimized file storage, today announced that Weka File System (WekaFS), with its Kubernetes Container Storage Interface (CSI) plug-in, has completed interoperability testing with Rancher Labs’ Kubernetes management platform. Together, the companies will offer enterprises an integrated, end-to-end tested solution for DataOps. […]
WekaIO Wins Best of Show for Innovative AI Application at Flash Memory Summit
Nov. 11, 2020 – WekaIO (Weka), a specialist in high-performance and scalable NVMe-optimized file storage, today announced that Weka AI was honored with Flash Memory Summit’s 2020 Best of Show Award for Most Innovative AI (Artificial Intelligence) Application at today’s FMS virtual awards ceremony. FMS, the storage industry conference focused on flash technology innovation, has recognized the […]
WekaIO Expands Cloud Offering with Kubernetes Container Storage Interface
CAMPBELL, Calif. – August 18, 2020 – WekaIO (Weka), provider of high-performance, scalable file storage for data-intensive applications, today introduced the Kubernetes (K8s) CSI (Container Storage Interface) plugin, allowing its customers to deliver Container-as-a-Service (CaaS) functionality utilizing the Weka File System (WekaFS), the world’s fastest and most scalable parallel file system for high performance workloads. Stateful applications […]
WekaIO Receives Artificial Intelligence Excellence Award
Today WekaIO announced that The Business Intelligence Group has named Weka a winner in its Artificial Intelligence Excellence Awards program. “the WekaFS file system can deliver 80 GB/sec of bandwidth to a single GPU server, scale to Exabytes in a single namespace, and support an entire pipeline for edge-to-core-to-cloud workflows. The system also delivers operational agility with versioning, explainability, and reproducibility along with governance and compliance with in-line encryption and data protection.”
WekaIO picks up steam in the HPC Market
Today WekaIO announced it closed its 2019 fiscal year with record velocity in revenues. The company grew revenue by 600% compared to 2018 with growth fueled by increasing adoption of NVMe-native storage systems to enable I/O-intensive applications in Artificial Intelligence (AI), life sciences, and financial analysis. “We are the only tier-1, enterprise-grade storage solution capable of delivering epic performance at any scale on premises and on the public cloud, and we will continue to fuel our momentum by hiring for key positions and identifying strategic partnerships.”
Innoviz to Accelerate AI for Autonomous Vehicles with WekaIO
Today WekaIO announced that Innoviz, a leading manufacturer of high-performance, solid-state Light Detection and Ranging (LiDAR) sensors and Perception Software that enables the mass-production of autonomous vehicles, has selected the Weka File System (WekaFS) to accelerate its Artificial Intelligence (AI) and deep learning workflows. WekaFS has been chosen by Innoviz to improve application performance at scale and deliver high bandwidth I/O to its GPU cluster.
Aiden Lab Chooses WEKA to Accelerate Genomics Research
Today WekaIO announced that Aiden Lab at the Baylor College of Medicine, a leading genome research facility, has selected the Weka File System (WekaFS) to accelerate its genomics research. “WekaFS has delivered a 3x improvement in performance at Aiden Lab and is enabling it to use its cloud infrastructure more effectively. WekaFS will improve overall productivity and empower researchers to become more efficient at analyzing results.”