Build and run federated machine learning and analytics across boundaries

Apheris is a federated machine learning and analytics platform built to orchestrate the complete lifecycle of machine learning and analytics across organizational and geographical boundaries.

Train stronger machine learning models

Apheris enables you to securely build and operationalize data applications and AI across organizations, industries, and borders, all while protecting privacy and IP.


Machine learning and analytics across organizational boundaries

Access federated data sets

Data is often spread across different departments or organizations and resides in a variety of systems. Access these datasets without the need to move data, all while protecting IP, ensuring data privacy, and eliminating the costs of centralizing data.

Leverage existing model and data pipelines

Bring your existing data pipelines and models (including pre-trained models) and run them with simple glue code on the Apheris platform. The SDK preserves your existing code and repo structure, so that you can easily keep the glue code file(s) together with your existing code using git, for example.

Data custodians stay in control of data, always

Data custodians are in full control of what happens with their data. They define asset policies with privacy controls that specify which user is permitted to run which computation on their data.

Integrates with your current infrastructure and ML stack

Integral to your ML stack

Apheris plugs into your existing infrastructure and integrates with other elements of your ML stack. You can leverage existing infrastructure for data and compute to reduce set up time. Apheris empowers you to use a variety of model training and serving tools of your choice – all across organizational boundaries.

Develop with the libraries you choose

Install the Apheris SDK on your local computer to use a variety of machine learning tools, frameworks and libraries on the Apheris platform. With the SDK you can leverage MLOps functionality for your federated computations – from automating your federated workflows to tracking your federated trainings in tools such as mlflow.

Integrates seamlessly with a thriving ML ecosystem

Apheris cleanly interfaces with downstream tools that leverage the analysis results or trained models. You can simply export results to use them in your data application, and also pass on trained models for model serving in other systems that are already used in your organization.

You always stay in full control of your models


Rely on strict asset policies, privacy controls and data protection measures to ensure private data stays private and regulatory requirements are always fulfilled.


Strict isolation between tenants, encryption of data, and frequent third-party penetration tests are just a few of the security safeguards implemented to prevent unauthorized access, data breach, or IP leak.
ISO 27001 certified.


Logging of data access and activities in the platform helps you fulfil audit and compliance responsibilities. Robust asset policies reliably control who can access data and for what purpose.

Explore the Apheris platform

Compute Gateway

Empowers data custodians to make their data accessible for machine learning and advanced analytics without sharing or moving data.

Compute Orchestrator

Allows for ML and analytics to run across multiple federated datasets while ensuring raw data is never returned from the data sources.


Use a variety of machine learning tools, frameworks, libraries, and bring your existing data pipelines and models to run them on the Apheris platform.

Trusted by our customers & partners

Certifiably secure

Any data, any size, anywhere

Want to learn more about how Apheris can help you power your infrastructure with federated machine learning and analytics?
Get in touch

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.


The Future of Data Products – 6 Industry Trends For Frontrunners

If you’ve been successful to date with building industry-leading data products, you don’t want to rest on your laurels. Instead, you should be trying to extend your lead. However, this can be daunting task in an industry that is in constant flux as the data, machine learning and AI landscape.


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.