Customers bring in Crater AI to develop new models based on the latest academic research, saving time and costs for AI/ML processing.
Using FlashBlade array from Pure Storage, Crater eliminated researchers' need to spend time configuring storage infrastructure for AI training runs.
Working on datasets of multiple terabytes, Crater found cloud training costly due to practical limitations, moving to shared capacity storage for efficient processing.
Crater faced challenges with in-house storage limits when feeding multiple models in parallel, highlighting the need for a more scalable solution like the FlashBlade array.
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