Jan Stuecke

Apheris Compute Gateway 3.0 provides an end-to-end federated analytics and machine learning solution that enables everyone to collaborate securely on highly sensitive data.

Open-source frameworks for federated learning are a great way of getting first hands-on experience. Here are our Top 7 with their respective pro and cons

Machine learning and AI needs domain-specific data to be trained for its various use cases. Often this data is sensitive and falls under various privacy regulations. In this article we will introduce the Apheris Compute Gateway as a solution for contributing sensitive data to ML projects.

Apheris Compute Gateway 3.2 provides a simpler way to create federated computations, adds federated nnUnet and cox regression and streamlines the data science experience.

Apheris Compute Gateway 3.0 provides an end-to-end federated analytics and machine learning solution that enables everyone to collaborate securely on highly sensitive data.

Computational governance enables machine learning on European healthcare data.
European hospitals face a unique challenge, balancing complex regulatory frameworks with the need to make valuable data available for reserach. Computational governance exists as a solution and enables compliant research on health data.

There is so much useful data around us. Yet most data is not accessible for machine learning or other large scale analytics. In this article, we will explore why this is and what needs to be in place to enlighten this dark data.