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We built a federated ML platform so you don't have to
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.
Federated
Data doesn't need to move protecting IP, ensuring data privacy, and eliminating the costs of copying or centralizing data.
Integrated
Works with existing tools and workflows allowing you to leverage existing data applications and model IP.
Scalable
Reliable, always-on automation for training models and building data applications across organizations, geographies, or use cases.
Full AI lifecycle
Supports the data and ML lifecycle, including preferred tools, languages, and infrastructure to reduce set-up costs and time to value.
Develop with the libraries you know and love
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.
APHERIS WORKS WELL WITH
Computations within a single organization and across multiple parties are supported:
- Federated analytics, from simple statistics to your custom advanced analytics pipelines.
- Federated learning, from simple ML, such as linear models or decision trees, to advanced Deep Neural Networks.
- Define your federation workflow and automate it - sequential, parallel, cyclic, clustered or custom federation flows - including your own aggregation functions.
- Federated computations can be aggregated via Apheris-provided functions or user-defined functions.
- Model validation across a single or multiple datasets. Easily compare the quality of local vs. global models.
Apheris SDK – Python library can be installed on your local computer
With the SDK, you can leverage MLOps functionality:
- Interface with MLOps libraries like mlflow for model management and model registry.
- Automate your federated workflows and re-run model training from your preferred job scheduler.
Private and secure
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.
Governed
Data custodians define who can do what with data. Logging of operations provides traceability for oversight and to meet audit and compliance needs.
Addressing today's challenges while unlocking value in data
Leverage existing model IP
Bring existing data pipelines and model IP and run against additional, federated datasets
Build new data or ML-enabled products
Access additional datasets to build new products or customize foundational models, for example.
Reduce risk and cost
By not needing to move data, you're reducing risk and removing the costs associated with centralizing or sharing data.
Remain compliant
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.
Working across organizational boundaries
Manufacturing
"With Apheris we found the perfect partner for exploring privacy preserving data analytics and optimization along the value chain with our customers."
Christian Winkler, Director Global Infinergy Operations BASF
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E-book - Federated Data Ecosystems in Pharma & Healthcare
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.
White Paper
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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.
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