WekaIO, the data platform provider for performance-intensive workloads, unveiled version 4.2 of the WEKA® Data Platform. The new release brings a variety of enhanced features and new capabilities to the company designed to increase the affordability and performance of next-generation technologies for WEKA’s customers. These include advanced data reduction and a new container storage interface (CSI) plug-in for stateful containerized workloads that can help customers dramatically lower their storage and operational costs.
WEKA Unveils Industry’s First Multicloud Data Platform for AI and Next Generation Workloads
WEKA, the Data Platform for AI company, unveiled the fourth generation of its software-based solution that delivers first to market results with artificial intelligence (AI), machine learning (ML) and other next generation workloads via a single, scalable, highly performant platform for hybrid cloud and edge environments – now available in any public clouds of their choosing.
Penguin Computing Announces OriginAI Powered by WekaIO
Penguin Computing, a division of SMART Global Holdings, Inc. (NASDAQ: SGH) and a leader in high-performance computing (HPC), artificial intelligence (AI), and enterprise data center solutions, announced that it has partnered with WekaIO™ (Weka) to provide NVIDIA GPU-Powered OriginAI, a comprehensive, end-to-end solution for data center AI that maximizes the performance and utility of high-value AI systems.
WekaIO Announces Cloud-Native, Unified Storage Solutions for the Entire Data Lifecycle
WekaIO™ (Weka), an innovation leader in high-performance, scalable file storage for data-intensive applications, today announced a transformative cloud-native storage solution underpinned by the fast file system, WekaFS™, that unifies and simplifies the data pipeline for performance-intensive workloads and accelerated DataOps.
Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads
Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.
Scaling Production AI
As AI models grow larger and more complex, it requires a server architecture that looks much like high performance computing (HPC), with workloads scaled across many servers and distributed processing across the server infrastructure. Barbara Murphy, VP of Marketing, WekaIO, explores how as AI production models grow larger and more intricate, server architecture gets more complex. Explore how tools like GPU clusters and more are moving the dial forward on AI.
AI Critical Measures: Time to Value and Insights
AI is a game changer for industries today but achieving AI success contains two critical factors to consider — time to value and time to insights. Time to value is the metric that looks at the time it takes to realize the value of a product, solution or offering. Time to insight is a key measure for how long it takes to gain value from use of the product, solution or offering.
AI Goes Mainstream
According to a recent Gartner survey, Artificial intelligence (AI) learning has moved from a specialized field into mainstream business use with 37 percent of respondents reporting their enterprises either had deployed AI or would do so shortly. WekaIO’s Barbara Murphy explores the path of artificial intelligence from the fringe to mainstream business practices. Find out what is driving AI growth and adoption.