Blog

MedTech AI revolution depends on computational access to fine-grained patient data to train enhanced ML models. Apheris provides governed, secure, and private computational access to data for ML, enabling fast and easy FDA approval for new MedTech data products.

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.

AI safety is not a future problem to solve, it’s a real problem right now. In this blog I'll explain the challenges we face as a society in developing and using AI, and propose some operating principles to help us leverage AI for social good.

"As AI adoption gathers momentum, it is essential we get in front of this generational technology and regulate it in a way that works for everyone – before it is too late."

In this blog we will explore the ways in which industries, including those reliant on consumer data, can use federated learning as an effective alternative to data sharing.

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.

11 February is the International Day of Women and Girls in Science. We interview Evelyn, Data Science Experience Lead at Apheris, and give you an insight into the life of a successful woman in science.

There are incredible opportunities to be seized through federated machine learning across many industries. While some early innovators have recognized the potential, many have yet to seize the opportunity before them.

Following its 77th session, the UN General Assembly championed collaboration as the cement that could strengthen individuals, organizations, and industries against both current and future challenges. Read on to find out how.

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.

Data is widely considered to be the lifeblood of modern organizations, but it’s only as valuable as the insights that can be mined from it. A collaborative data ecosystem helps organizations come together to mine much-needed insights.

In order to rise above today’s challenges, manufacturers need to come together, pool their collective assets and insights, and create modern solutions to modern problems.

As we near the end of the year we take a look at key themes from 2022 and what to expect in 2023. Here are our top predictions for the year ahead.

More businesses are waking up to the value of data and the necessity of data collaboration in harnessing its power.

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.

Seed extension round led by Octopus Ventures to drive growth of platform enabling organizations to unlock terabytes of valuable data risk-free

The Apheris Platform enables multiple organisations to extract value from each other’s decentralised data sets and overcome regulatory, technical, and commercial challenges.

In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.

PETs are massively changing how we operate, and how we have to think about the data and AI landscape. Introducing such a game-changer into large enterprises has to be done with the highest precision, and a lot of foresight.

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.

There is only value in data if it can be used, and if there is appropriate access to that data when it is needed. Learn how you can turn decentralized, federated data into a complementary strategic data asset.

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

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

If you’ve been successful to date with building industry-leading data products, you don’t want to rest on your laurels. Instead, you should be trying to extend your lead. However, this can be daunting task in an industry that is in constant flux as the data, machine learning and AI landscape.