Developer tools for federated learning and analytics

Machine learning and analytics on distributed data require complex infrastructure and tooling that drain valuable developer resources. abstracts away the complexity, making it easier than ever to integrate federated learning and analytics into your product.

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Built for builders. was created with developers in mind. Our SDK makes it simple to build features powered by federated learning and analytics, so that you can focus on your core product.
Comprehensive APIs: Seamlessly integrate the end-to-end federated machine learning and analytics workflow into your product, from connecting datasets to training models.
Developer resources: Take advantage of our extensive and easy-to-use documentation, templates, and tutorials, so you can make the most out of our developer tools.

Federated learning and analytics in your product in less than a week.

Accelerate your product roadmap with the production-ready MLOps extension for machine learning and analytics on distributed data. Deploy in your product without making changes to your existing architecture, or your customers' infrastructure.

Managed Infrastructure

Automated provisioning and management of federated learning infrastructure in the cloud.

Network Administration

Simple management and usage APIs to generate secure tokens, and connect new datasets.

Data Science Tools

Out of the box support for almost any model type, analytics, and differential privacy without additional dev work.

Simplicity, without losing flexibility.

You don't need to sacrifice functionality for ease of use. abstracts away the complexity of machine learning and analytics on distributed data, while offering the flexibility to meet your product requirements.
Flexible architecture options: Deploy federated learning and analytics on any trusted server - on prem or in the cloud. Choose from multiple privacy enhancing technologies to optimize for privacy and security requirements.
Expansive data science tooling: Enable model training using most model types, including deep learning models, GLMs, CNNs, LSTMs, Transformers, FFNNs, and decision trees. Control differential privacy settings, and even decide which federated average strategy to use.


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