Blog

With today's release, we continue our journey to provide the simplest, most secure solution for building and joining data networks in life science and drug discovery.

Pharma and biotech companies often lack enough data within their own organizations, limiting AI model accuracy. Read more about my discussions with pharma leaders at this years Drug Discovery Innovation Forum DDIF on overcoming the data access dilemma.

Secure data collaboration brings new capabilities to the pharama and biotech community. Read about applications, use cases and challenges in collaborative settings and how Apheris can help.

Great news, everyone! We released Apheris 3.3. With prior releases, we set the foundation for two really useful improvements for our users today — derived Datasets and Multi-Gateway Organizations.

We are excited to announce that Apheris has officially joined the Global Alliance for Genomics & Health (GA4GH) as an organizational member.

Our journey to secure and standardize GitHub repository configurations at Apheris using Infrastructure-as-code.

Federated Learning and Data Mesh enable decentralized model training with domain-specific governance. This integration improves data privacy, scalability, and regulatory compliance, addressing limitations of traditional systems in regulated sectors where sensitive data is involved.

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.

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

Discover the power of Privacy Enhancing Technologies (PETs) and why they're crucial for your enteprise ML/AI projects involving sensitive or regulated data. We'll demystify PETs with clear examples, equipping you with the knowledge to make informed decisions on how to best protect your data and enhance your products.

In December, the annual NeurIPS conference took place. With the biggest conference in machine learning research happening, I was curious to see what was going on in the world of federated learning. Here are my 5 take-away on how federated learning can be applied in industry today.

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 research. Computational governance exists as a solution and enables compliant research on health data.

The Apheris Trust Center serves as a comprehensive resource for organizations seeking to uphold the highest standards of security and data privacy. It offers guidance and our certifications and attestations, including ISO 27001 and SOC 2, that customers value as essential components to fulfil their compliance obligations.

Apheris achieves SOC 2 Type I attestation, reaffirming its commitment to data security and privacy, and the comprehensive measures we have implemented to protect sensitive information and ensure the highest level of security.

As AI reshapes many industries, the balance between innovation, security, and compliance has become paramount, especially in regulated sectors.

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.

Exploring the EU AI Act, a pivotal regulation addressing AI's multifaceted nature, this blog outlines risk levels, high-risk system requirements, and preparation for compliance. Apheris's solution for various governance, privacy, and security obligations is highlighted, emphasizing a shared commitment to responsible AI.

Reaching universal agreement on how to regulate AI is impossible. But a carefully designed cyclical process can lead to efficiencies in an enterprise's journey towards trustworthy, accountable, and sustainable AI. In this blog, I navigate the regulation space through the eyes of a data custodian.

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.

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.

Companies are benefiting from federated learning by being able to access data without 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.

The clock is ticking. The time to act on climate change is now. In this blog, I'll take a closer look at practical thoughts on climate thinking and action in organizations.

"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'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.

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.

In conversation with ChatGPT about the relationship between large language models (LLMs) and federated learning.

Together with the German Federal Office for Information Security we've developed frameworks and recommendations for ML practitioners to help secure ML models and maintain appropriate security measures.

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.

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.

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.

Open AI's ChatGPT is very popular, yet not ready for enterprise adoption due to accuracy, security & privacy concerns. By refining large language models this can be tackled. For organizations that operate in a regulated space, federated learning can be used to train on distributed data to unlock value while maintaining privacy.

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.

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.

Our focus on sustainability extends beyond the impact we have on our customers, and our environmentally conscious mindset is at the core of Apheris’ work culture.

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.

How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.

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

Collaborative data ecosystems in pharma securely connect life sciences and healthcare data to drive AI-powered insights or advanced analytics on these distributed datasets. They enable shared use of real-world data without exposing sensitive information, overcoming data silos while ensuring privacy and compliance.

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.