Imagine a data transform that securely accesses multiple siloed data sources using a shared schema. With DataFleets, stop re-writing ETL.
Leverage data across domains to train machine learning models. The individual datapoints never leave their source, only the updates to the shared model.
From logistic regression to stochastic gradient descent, our library of data science tools is automatically loaded into all notebooks, giving you access to immense power at your fingertips.
Share intelligence across your fleet of devices with unified queries that share insights across devices from a single command center.
With federated computation, the data remains in place and is only accessed via secure data science operations that can mask and random-sample sensitive data fields.
By moving only models and not data, you can lower the bandwidth requirements for modern deep learning operations, allowing you to modernize your intelligence stack more rapidly.
Datafleets provides a web-based notebook editor for data scientists to quickly and effectively test hypotheses across multiple data sets, in collaboration with others
If you want DataFleets features in your own code or editor, simply import the PyFleets library and use your own tools to format and post-process data.
Your queries and commands are secured at a granular level, allowing multiple data scientists with diverse permissions apply their own algorithms to data without fear of violating an SLA or EULA.