Unleash the potential of Data
With Acuratio's Multicloud Federated Learning Platform organizations around the world are able to unlock the value of data by combining datasets without compromising privacy.
docker-compose -f acuratio.yml up
Breaking Down Silos
Many companies anticipate huge benefits from machine learning, but cannot access the data they need due to privacy or regulatory concerns. Acuratio’s platform allows companies to leverage and combine different types of data to develop more accurate models or compute aggregate statistics, while preserving both model and data privacy.
Horizontal Learning
Train a ML Learning model with distributed examples of the same data with Split Learning or Federated Averaging.
Example: Data localization, fraud, risk, forecasting…
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Vertical Learning
Combine two or more data sources with a common set of users but different features with Split Learning and Private Set intersection.
Example: Pricing, retention…
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Private Analytics
Keep individual information private while allowing organizations to compute aggregate statistics or segment your audience, Private Join & Compute.
Example: conversion, cohorts...
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Multicloud Federated Learning
Utility and privacy without tradeoffs, get insights or improve your machine learning models by incorporating new sources of data, without sacrificing model or data privacy.
Ease of Use
From data to production, create project, connect data, train model, test model.
- Install the platform
Connect your data lake. On the cloud or on-prem. - Train by adding a few lines of code.
Deploy your current TensorFlow models or compute aggregated statistics. - Unlock your data!
Share, monetize or get insights from your data.
Features
The most complete Federated Learning platform.
Privately train, deploy, serve, and manage Machine Learning Models.
| Teams Edition | Enterprise Edition | |
|---|---|---|
| Vertical Federated LearningDifferent configurations that suit your needs. | ||
| Horizontal Federated LearningFederated Averaging and Split Learning for TensorFlow or Keras. | ||
| Differential PrivacySeamless integration during training. | ||
| Secure Aggregation ProtocolFor cryptographically private Federated Learning. | ||
| Role Based AuthenticationGranular control over access to data, models, and results. | ||
| Focused UpdatesSparse ternary compression and Random Rotation Matrices. | ||
| GPU SupportTraining and serving performance and efficiency. | ||
| CollaborationRole Based Access Control and Team Management. | ||
| Audit LogsMonitor access to your data. | ||
| MulticloudOn-premise, cloud and hybrid deployments. | ||
| Model ServingSeamless deployment of ML models. | ||
| Personalized SupportGet quick responses and solutions from our experts. |
“Acuratio is democratizing access to Advanced Private Learning.”
Ramesh Raskar
Director of the Camera Culture research group at MIT Media Lab
“The Best Production-Grade Federated Learning Platform”
Jose Luis Agundez
Global Head of Data Architecture at Banco Santander
Ask for a Demo!
Describe your problem or use case, and we will get in touch with a solution.
Something went wrong! Please, contact us at contact@acuratio.com