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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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