Deliver powerful new models and analytics using distributed data.

Access more customer and partner data for machine learning and analytics with MLOps extension for distributed data.

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Join the federation nation.

With federated learning and analytics, you're not limited to using data that is stored on a central server.
Data never moves: Models are trained where the data resides and only model parameters leave the local server, so that data custodians retain full control of the raw data.
Privacy-preserving: Differential privacy and other privacy enhancing technologies ensure that no identifiable information can be inferred during or after model training.
Easily configurable: You get to control all of the important variables. Configure your own custom models, define your differential privacy settings, and even choose your federated aggregation strategy.

As easy as working with centralized data

With tooling for the entire data science workflow, provides a seamless experience from exploration to model training.
Exploratory data analysis: Create visualizations and calculate basic statistics on individual datasets or in aggregate.
Feature engineering: Calculate new features on distributed datasets.
Model training: Configure and train almost any model type, including deep learning models, GLMs, CNNs, LSTMs, Transformers, FFNNs, and decision trees.

The quickest path to federated learning and analytics on your platform

Building federated learning and analytics infrastructure with open source tools is complex and time consuming - we're talking about weeks or months to deployment. As a production-ready SDK, lets you start training models and running analytics across distributed data on your platform in as little as three days.

API-based platform

Seamlessly integrates into your product.

Managed infrastructure

Avoids developer time to provision and manage infrastructure.

Built-in tooling

All network administration and data science tooling is already included in the platform.


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