Access more data. Protect data privacy. Shorten time to value.
Run machine learning on distributed data for faster insights and better outcomes without the cost, concentration risk, long lead times, and privacy concerns of centralizing data.
Safe Federated Learning on Distributed Datasets
Devron brings federated machine learning technology to enterprises with our decentralized cloud data science platform, Synergy.
Train algorithms on decentralized data
Centralize insights not data
Preserve privacy and increase generalizability
Our Synergy Platform
1. Access All of Your Data
Synergy allows your data to reside in a decentralized architecture to maximize data privacy, while still giving you the ability to generate valuable insights.
2. Federated Learning Operations
Synergy is the first cloud platform to offer a user experience focused on decentralized (federated) machine learning in order to simulate your enterprise data silos and test various federated learning machine algorithms. This allows for data science teams to test privacy preserving techniques quickly to avoid privacy leakage in production applications.
3. Fits Your Data Stack
Synergy seamlessly integrates inside your Cloud Data Lakes, Warehouses and Data Processors to enable decentralized machine learning. Our Python SDK provides developer ergonimcs to integrate with Snowflake, AWS, Databricks/MLFlow, Spark, Jupyter and machine learning frameworks such as Tensorflow, PyTorch and Scikit-Learn.