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Technologies

Accelerate Training and Inference Workloads

Whether it comes to autonomous driving, speeding up reporting of cancer data, or accelerating data science discovery, enterprises are adopting AI, ML, DL and NLP technologies to process, analyze and act on rapid influx of new, complex and large data. These complex and rapidly expanding AI use cases require ingestion of vast amounts of data to train the algorithms which drives the demand for storage infrastructure that is powerful, flexible and highly scalable.


Foundation for Future Use Cases

With Panasas next-generation ActiveStor solutions, you can accelerate time to decision by taking advantage of the parallel file system running across multiple nodes and eliminating bottle necks for large file transfers and ensuring consistent performance regardless of scale. You can put both training and inference capabilities on the same platform while ensuring uniform view of data across all phases of the AI process such as ingestion, preparation and learning. In addition, Panasas hybrid storage arrays are designed to efficiently store data across flash and spinning disks to balance cost and system performance.

Artificial Intelligence

Most complex AI solutions make use of sophisticated neural networks that
require and use massive computing power. This in turn places demand for more intelligence in the storage infrastructure that must adapt to this never-ending cycle of demand for increasing functionality and more throughput and performance.

Deep Learning

When it comes to deep learning, specifically training, accuracy and time-to-model matter, regardless of the size of the data sets. Training scales in a near-linear fashion which requires optimized and linearly scalable storage infrastructure.

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