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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.
Buyer's Guide to Secure Data Collaboration
Thought leaders across industries are sure: Secure data collaborations with multiple parties are the next source of getting a decisive competitive edge. But only but few data leaders and teams know exactly what to look for to get these projects going. To be successful, it is important to make balanced and well-considered, but quick decisions. That's why this buyer's guide will help you evaluate platforms and highlight aspects that are necessary for valuable AI and analytics across companies.
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. Although machine learning in its various forms has been deployed for drug discovery and development for several decades, the era of Big Data has created a niche for Deep Learning. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.
Privacy-preserving Data Science on QSAR models
This case study demonstrates the utility and effectiveness of Apheris’ platform and services for data science on not directly accessible and distributed data. An integral part of our platform is the implementation of federated learning, which decentralizes the learning algorithms to access a number of different data sets while maintaining privacy. This process eliminates the need to pool sensitive data, which has been a major hurdle in practice due to a lack of trust between different data providers.
Federated Learning on Vertically Distributed Healthcare Data
If organizations with complimentary data sets could join their databases together, it would become possible to train strong machine learning models that could outcompete current industry standards. However, data sets are protected due to their inherent value. They also often contain intellectual property, which cannot be disclosed or shared with other parties. Furthermore, medical data can contain sensitive and personal identifiable information, which is governed by regulatory frameworks that often prevent companies from easily sharing data within their professional networks.