Any data, any size, anywhere
Want to know more about how you can benefit from the Apheris federated machine learning and analytics platform?
Our federated machine learning and analytics platform allows you to work with federated data, including highly sensitive data. Because data doesn't need to move, we enable you to create your own network of trusted data partners across organizational and geographical boundaries.
Data doesn't need to move protecting IP, ensuring data privacy, and eliminating the costs of copying or centralizing data.
Works with existing tools and workflows allowing you to leverage existing data applications and model IP.
Reliable, always-on automation for training models and building data applications across organizations, geographies, or use cases.
Supports the data and ML lifecycle, including preferred tools, languages, and infrastructure to reduce set-up costs and time to value.
The Apheris SDK allows you to leverage a variety of machine learning tools, frameworks and libraries. Bring your existing data pipelines and models (incl. pre-trained models) and run them with simple glue code in the Apheris platform. The SDK preserves your existing code and repo structure, so that you can easily version control the glue code file(s) together with your existing code using git
The Apheris SDK includes dedicated ML and data science modules, for privacy preserving statistics or privacy preserving ML for example, and empowers you to do foundation model fine-tuning and more.
With the SDK, you can leverage MLOps functionality:
Our federated infrastructure is built with data privacy and security being central to its design. Asset policies, privacy controls, and data protection measures ensure sensitive data stays private, IP is protected, and compliance requirements are fulfilled.
Horizontal and vertical data partitioning.
Data custodians define who can do what with data. Logging of operations provides traceability for oversight and to meet audit and compliance needs.
Bring existing data pipelines and model IP and run against additional, federated datasets
Access additional datasets to build new products or customize foundational models, for example.
By not needing to move data, you're reducing risk and removing the costs associated with centralizing or sharing data.
Data remains with the data custodian and asset policies determine who has access to what. With all operations logged, you'll have what you need to meet compliance and audit needs.
"With Apheris we found the perfect partner for exploring privacy preserving data analytics and optimization along the value chain with our customers."
Want to know more about how you can benefit from the Apheris federated machine learning and analytics platform?
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.
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.
Following its 77th session, the UN General Assembly championed collaboration as the cement that could strengthen individuals, organizations, and industries against both current and future challenges. Read on to find out how.