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In the current data-driven landscape, regulations like the EU AI Act are increasingly important due to the growth of AI and global privacy concerns. The Act provides a legal framework for AI risks and applies to businesses, both within and outside the EU. This short ebook argues that preparing for this regulation is not just about compliance but also about leadership and innovation. It will discuss the Act's risk-based approach, the urgency of preparations, and offer insights on achieving compliance through Apheris.

In a world where data is pivotal, its full potential is often overshadowed by risks. As foundation models proliferate, data emerges as the unique advantage. The challenge? Leveraging it safely. Our short ebook introduces Apheris' solution for secure computational access to data, connecting federated data sets for ML without the risks of sharing or centralization. A primer for data readiness for federated learning, prioritizing security and privacy.

The EU AI Act marks a crucial step in regulating AI, addressing inherent risks and fostering innovation. Applicable to entities within and outside the EU, this article delves into the EU AI Act's structure, the urgency of preparation, and offers insights into achieving compliance.

A leading data aggregator in healthcare research partnered with Apheris to provide life sciences customers with granular access to patient data while ensuring GDPR compliance. Apheris enabled fully federated machine learning, optimizing the model training process and enhancing customer satisfaction by addressing long-standing GDPR issues. This partnership illustrates Apheris's potential to unlock data assets securely and efficiently.

An industry-leading data-as-a-service (DaaS) provider, operating globally and facing increasing demand for machine learning (ML) capabilities, partnered with Apheris to provide programmatic access to data while ensuring GDPR compliance. Apheris's non-movement of data and computational access governance led to several benefits, including quicker insights for their customers, reduced time and cost on regulatory compliance processes, and increased revenue by offering programmatic access for compliant ML as an added product feature.

Discover the true potential of federated machine learning and analytics, implement it seamlessly with Apheris, and ensure data privacy and security while building cutting-edge ML models.

A detailed look into how data-hungry organizations no longer need to rely on data sharing for collaboration, and can preserve privacy and IP while working across boundaries. Big or small, organizations the world over are waking up to the value of federated data.

Privacy and security considerations are an important part of adopting a federated infrastructure for machine learning and analytics. This whitepaper outlines the various security techniques and privacy controls used by Apheris to safely collaborate and build data applications and AI across organizational and geographical boundaries.

Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing. This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare.

The world's biggest challenges are going to be solved in Collaborative Data Ecosystems. Learn how in our free-to-access infographic!

Data collaborations are required if we're going to solve our biggest challenges. This guide discusses challenges with the status quo and provides answers in working with federated data and breaking down barriers while establishing a collaborative data ecosystem.

The world's biggest challenges are going to be solved in Collaborative Data Ecosystems. Learn about the most important facts in our infographic!

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.

Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

Secure data collaborations with multiple parties are the next source of getting a competitive edge. However, only a few data leaders know what to look for to get projects going. This buyer's guide will help you evaluate platforms and highlight considerations for AI and analytics across organizational boundaries.

DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.