TRENDING compute engine services in a scalable system that was built from the ground up for the future of AI .
While generative AI and Large Language Models ( LLMs ) have introduced the world to the early capabilities of Artificial Intelligence , LLMs are limited to performing routine tasks like business reporting or reciting information that is already known .
The true promise of AI will be realized when machines can recreate the process of discovery by capturing , synthesising and learning from data – achieving a level of specialisation that used to take decades in a matter of days .
The era of AI-driven discovery will accelerate humanity ’ s quest to solve its biggest challenges .
AI can help industries find treatments for disease and cancers , forge new paths to tackle climate change , pioneer revolutionary approaches to agriculture and uncover new fields of science and mathematics that the world has not yet even considered . reducing infrastructure deployment complexity for business intelligence and reporting applications but are not built to meet the needs of new Deep Learning applications .
This next generation of AI infrastructure must deliver parallel file access , GPU-optimized performance for neural network training and inference on unstructured data and a global namespace spanning hybrid multicloud and edge environments – all unified within one easy to manage offering in order to enable federated Deep Learning .
The VAST Data Platform was built with the entire data spectrum of natural data in mind – unstructured and structured data types in the form of video , imagery , free text , data streams and instrument data – generated from all over the world and processed against an entire global data corpus in real-time .
This approach aims to close the gap between eventdriven and data-driven architectures by providing the ability to :
As such , enterprises are increasingly turning their focus to AI applications .
Today ’ s existing data platforms have become popular for global enterprises , dramatically
• Access and process data in any private or major public cloud data center
• Understand natural data by embedding a queryable semantic layer into the data itself
• Continuously and recursively compute data in real time , evolving with each interaction
For more than seven years , VAST has been building toward a vision that puts data – natural data , rich metadata , functions and triggers – at the center of the VAST Disaggregated Shared-Everything ( DASE ) distributed systems architecture .
DASE lays the data foundation for Deep Learning by eliminating trade-offs of performance , capacity , scale , simplicity and resilience to make it possible to train models on all of an enterprise ’ s data .
By allowing customers to now add logic to the system – machines can continuously and recursively enrich and understand data from the natural world .
To capture and serve data from the natural world , VAST first engineered the foundation of its VAST DataStore platform .
The exabyte-scale DataStore is built with best-in-class system efficiency to bring archive economics to flash infrastructure – making it also suitable for archive applications . Resolving the cost of flash storage has been critical to laying the foundation for Deep Learning for enterprise customers as they look to train models on their proprietary data assets .
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