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What's New

  • Fully-supported containerized software.
  • Three software SKU’s are available 5, 10 and 20 nodes with a 1-year subscription license.

Key Features

Preserve Privacy for Machine Learning

With HPE Swarm Learning, raw data is not transferred to a central location or between locations: source data stays at the data source.

The learnings are shared between participating nodes, preserving data privacy, and improving insights.

Decentralized Machine Learning

HPE Swarm Learning unlocks machine learning with features like global-state merge, without needing a centralized node for training.

Collaborative model training at edge devices.

Parameters are merged at the edge or data source.

Decentralized architecture increases reliability: there is no single point of failure.

Machine Learning at the Edge Where Data Resides

HPE Swarm Learning preserves network bandwidth, as learnings are at the data source.

Near or at data source enables prompt inferences.

Improved Efficiency for Model Training

In case of failure, HPE Swarm Learning allows the remaining nodes to continue machine learning. As the node comes up, it continues participation.

No back-and-forth transfer of data, saving bandwidth and data duplication.

Enables prompt inferences at the data source.

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How can we help

Get advice, answers, and solutions when you need them. For general questions, email us at hpestore.quote-request@hpe.com

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