Any data, any size, anywhere
Want to learn more about how Apheris can help you power your infrastructure with federated machine learning and analytics?
Apheris enables you to securely build and operationalize data applications and AI across organizations, industries, and borders, all while protecting privacy and IP.
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.
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 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.
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.
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.
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.
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.
Empowers data custodians to make their data accessible for machine learning and advanced analytics without sharing or moving data.
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.
Want to learn more about how Apheris can help you power your infrastructure with federated machine learning and analytics?
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