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Partnering with NetApp enables Pong Yuen to democratize the capability of building the metaverse for its customers. Pong Yuen’s customers leverage ONTAP AI for digital twins and generating synthetic data, which they can use to train their machine learning models and accelerate decision-making.
NetApp ONTAP AI allows Pong Yuen to build a certified AI cluster leveraging proven architecture solutions from NVIDIA and NetApp. It consolidates a datacenter’s worth of analytics, training, and inference computing into a single, integrated system. Pong Yuen’s customers can easily get their AI or machine learning workloads up and running without worrying about setting up their own machine learning infrastructure.
Partnering with NetApp allows us to democratize the capability of building the metaverse for our customers.
Anri Kitami, Software Engineering Specialist, Pong Yuen
Pong Yuen’s customers generate massive amounts of synthetic data that is continuously generated in the metaverse. They need a best-in-class data solution to deliver extreme performance, simplify deployment, and speed return on investment. ONTAP AI offers an exceptional data-reduction ratio and saves Pong Yuen’s customers a lot of capacity, enabling them to train their machine learning models more efficiently.
With FlexClone, Pong Yuen’s customers can quickly and easily clone datasets for testing across different environments, greatly simplifying and accelerating machine learning and deep learning development. ONTAP AI gives Pong Yuen’s customers data portability and freedom from edge to core to cloud while enabling easy integration for collection of IoT data from edge devices to train machine learning models at the core.
With FlexClone, Pong Yuen’s customers can quickly and easily clone datasets for testing across different environments, greatly simplifying and accelerating machine learning and deep learning development. ONTAP AI gives Pong Yuen’s customers data portability and freedom from edge to core to cloud while enabling easy integration for collection of IoT data from edge devices to train machine learning models at the core.
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