Apheris achieves top information security certification, ISO 27001
Certification highlights our mission and focus on best practices for federated machine learning and analytics, enabling organizations to securely build and operationalize machine learning and data applications across boundaries.
Apheris raises €8.7m to power development of smarter AI and collaboratively solve the world’s biggest challenges
Seed extension round led by Octopus Ventures to drive growth of platform enabling organizations to unlock terabytes of valuable data risk-free
Apheris launches platform to unlock data and enable collaboration to solve the world’s biggest problems
The Apheris Platform enables multiple organisations to extract value from each other’s decentralised data sets and overcome regulatory, technical, and commercial challenges.
Data without borders
Organizations across the globe are quickly waking up to the need for safe, hassle-free data collaboration without the limitations, barriers, and security issues associated with data sharing. Apheris' Compute Gateway is making secure data collaboration across boundaries a reality.
The Rise of Federated Learning
Companies are benefiting from federated learning by being able to access data without the costs, privacy and IP risks associated with copying or centralizing data.
The Why, When, Where and How of Federated Learning
In this blog, we'll explore how Federated Learning can solve the common challenges around data access. It’s no secret that lack of access to the right data is one of the leading causes of project failures; what is surprising is that the right data often exists in data silos that, frustratingly, data scientists cannot access.
What Are Collaborative Data Ecosystems?
Enterprises across industries are realizing that collaboration on data, machine learning and data science is the key to solving the biggest challenges of our time. This leads to a massive rise of collaborative data ecosystems around the globe. Read more if you want to know what collaborative data ecosystems are
The Five Qualities of Collaborative Data Ecosystems
Over the past decade, organizations have focused heavily on implementing the culture, tools, and processes to create value from data with data science and machine learning within the enterprise. But this transformation doesn't intend to stop at corporate boundaries. Read the article to find out more
Challenges with implementing federated learning
Explore some of the common challenges organizations face when implementing federated learning and key considerations that can help them overcome those challenges and reap the rewards of working with federated data.
Data collaboration vs data sharing
It's often assumed that data sharing is required in order to derive value from data held across multiple organizations in a value chain. However, data sharing comes with challenges. Working with federated data, however, addresses these challenges and allows for better collaboration across organizational boundaries.
The State of Data Collaboration – why we are asking for your help
Participate in our survey and shape the future of Data Collaboration.
For a completed survey you will recieve a report that will help to benchmark yourself and your company on your collaborative ecosystem efforts.