A bit of history…

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In the beginning, we had individual computers, which were great for doing things alone...

We made computers that networked to databases so that we could store mountains of data, but these data silos became bloated and unmaintainable...

We developed grid computing to create super computers from many machines together, but these grids were unreliable and failed in unexpected ways...

We created cluster computing to merge data and computation into one space that was fault tolerant, but managing clusters was a pain...

We created data virtualization to combine our clusters and data sources, but it made machine learning and edge computation cumbersome...

Federated computing unifies mutiple clouds, data centers, and edge devices into a single federated resource, so you can put all your data to work...

...but federated computing is just a set of building blocks. What new and exciting story will you tell with it?

The DataFleets Computing Stack

Notebook Editor

The DataFleets Notebook Editor allows queries and computation to be conducted on remote data sources, with rich visualization and governance features.

PyFleets

DataFleets' python API allows you to access the full breath of federated intelligence capabilities on your existing IDE or workflow system.

Fleet Coordinator

The Fleet Coordinator sits in your data silo infrastructure, securely managing federated jobs and aggregating machine learning gradients.

Fleet Runner

The Fleet Runner is an extensible endpoint daemon that can be placed in any silo or edge device, with a low-level SDK to match a variety of environments.

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