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Collaboration Collaborative data ecosystems
White Paper
Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
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.
Infographic
Infographic: What are Collaborative Data Ecosystems?
The world's biggest challenges are going to be solved in Collaborative Data Ecosystems. Learn how in our free-to-access infographic!
Case Study
Case Study: Manufacturing
Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.
Guide
Buyer's Guide to Secure Data Collaboration
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.
White Paper
Federated Learning on Vertically Distributed Healthcare Data
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.
Guide
Guide to Collaborative Data Ecosystems
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.
White Paper
Privacy-preserving Data Science on QSAR models
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.
White Paper
Privacy-preserving Data Ecosystems in Support of Drug Discovery
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.
Case Study
Case Study: Pharma
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.
Case Study
Case Study: Healthcare
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.
Guide
Infographic: What are Collaborative Data Ecosystems?
The world's biggest challenges are going to be solved in Collaborative Data Ecosystems. Learn about the most important facts in our infographic!

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