A Revolutionary Approach to Unstructured Data Management
The Data Platform For
Introducing the world’s first data platform built for deep learning AI
In August 2023, we introduced a breakthrough approach to data-intensive computing that serves as the comprehensive software infrastructure required to capture, catalog, refine, enrich, and preserve data assets at any scale.
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The first new scale-out architecture in 20 years, designed for the next 20 years of AI and big data computing.
VAST is allowing us to put all of our rendered assets on one tierless cluster of storage, which offers us the ability to use these petabytes of data as training data for future AI applications. We’ve already moved all of our denoising data, ‘finals’ and ‘takes’ data sets onto the VAST Data Platform, specifically because of the AI capabilities this allows us to take advantage of in the future.
Eric Bermender
Head of Data Center & IT Infrastructure,, Pixar Animation Studios
AI is a big priority for us here at Zoom, and we’re working with VAST on efficiently building and training our AI/ML models across multiple unstructured datasets of video, audio and text data. Automation is the key, and the VAST Data Platform allows us to build beyond the capabilities that we’ve already built to deliver a frictionless global communication experience.
Vijay Parthasarathy
Head of AI/ML, Zoom
As we see AI proliferate to enterprise companies, there’s a need for an AI platform that is truly enterprise-grade. NVIDIA has a great partnership with VAST, and we’re looking forward to working with the team as they’ve taken this bold vision to build beyond storage to deliver a platform that helps to bring structured and unstructured data together in a unified, global namespace.
Manuvir Das
Vice President, Enterprise Computing at NVIDIA
Allen Institute
Taking advantage of new advancements in AI will be pivotal to help us make sense of all of this data, and the VAST Data Platform allows us to collect massive amounts of data, so that we can ultimately map as many neural circuits as possible - and its mechanisms for collaboration enable us to rapidly share that data around the world.
David Feng
Director of Scientific Computing, Allen Institute