Any data, any size, anywhere
Want to learn more about how to benefit from working with a federated data infrastructure?
Get in touch
Federated infrastructure
The Apheris Compute Gateway is deployed into your environment where you register data. This includes robust asset policies that allow you to determine the right level of privacy and security for your data, who can access that data and for what purpose. Combine this with logging of interactions and you can be sure you remain compliant and avoid the risk of a data breach or IP leak.
No need to centralize data
With no need to share or centralize data, and with robust asset policies, you remain in control of your data including who has access and for what purpose. Logging of every interaction allows for traceability and audit to meet regulatory requirements.
Work across organizational and geographical boundaries
Because data doesn't need to move, and with the right privacy-enhancing technologies in place, you can provide access to even the most sensitive data.
Unlock inaccessible data for machine learning
Allow complementary data value to be unlocked by allowing previously inaccessible data to be included in machine learning models and analytics.
Watch: Data Custodian Demo
Learn how Apheris provides privacy, security and governance to ML data, allowing custodians to provide access with peace of mind.
Apheris Compute Gateway - no need to move data
Leverage federated data sources
Apheris makes it easy to leverage federated data for machine learning and analytics. Data doesn't need to move protecting IP, ensuring data privacy, and eliminating the costs of centralizing data.
Deploy anywhere
Each data custodian deploys the Compute Gateway within their existing infrastructure (either cloud or on-prem) and connects it to the data. Apheris provides abstractions for the data sources and each party controls which computations are allowed to run on their data.
Flexibility on compute
The Compute Gateway empowers external parties to run computations on the connected data. Included is an environment from Apheris (based on Python/Kubernetes) containing statistics and machine learning modules designed for federated computations. Alternatively, connect to external environments, such as Spark.
WORKS WITH YOUR DATA SOURCES
DEPLOY IN YOUR EXISTING INFRASTRUCTURE
CHOOSE YOUR PREFERRED COMPUTE ENVIRONMENTS
Data custodians stay in full control, always
Data custodians are in full control over 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.
For any computation request from a user, asset policies are validated at multiple points throughout the platform. Additionally, data custodians can approve incoming compute requests and control what results leave their Compute Gateway. This ensures that results are returned to the data scientist without exposing data or IP.
For any computation request from a user, asset policies are validated at multiple points throughout the platform. Additionally, data custodians can approve incoming compute requests and control what results leave their Compute Gateway. This ensures that results are returned to the data scientist without exposing data or IP.
Technical specification
- Deployment using infrastructure-as-code (Helm Charts, Terraform) provided by Apheris into a Kubernetes cluster.
- Apheris provides reference deployments for all major cloud providers (AWS, GCP, Azure) and on-premises deployments.
- As an alternative to deploying the Compute Gateway into an existing Kubernetes cluster, Apheris also provides a standalone installer, which includes a lightweight Kubernetes distribution.
Supports large computational workloads. For example, large foundational ML models.
Stay compliant
Work with customers across organizational and geographical boundaries while remaining compliant with all privacy regulation and protecting IP.
Data doesn't need to move
Federated infrastructure means that there's no need to share or centralize data. By not moving data, the data custodian remains in control of data at all times.
Integrate into your tech stack
Apheris can be deployed into different environments and interfaces with existing systems and data sources.
Private & secure
Asset policies, including fine-grained access controls, determine who has access and for what purpose.
Multiple privacy-enhancing technologies allow users to set the appropriate level of privacy for the type of data so that maximum value is extracted from any dataset.
Each action on the Apheris Platform is logged for full auditability and enforcement of the asset policies.
Multiple privacy-enhancing technologies allow users to set the appropriate level of privacy for the type of data so that maximum value is extracted from any dataset.
Each action on the Apheris Platform is logged for full auditability and enforcement of the asset policies.
Governed
Data doesn't need to move so data cusodians always stay in full control of their data and data sovereignty is retained. Logging of operations ensures traceability for audit and compliance needs.
Trusted by our customers & partners
Address today's challenges. Make data accessible.
Reduce risks and costs
Reduce risk and remove overhead costs associated with centralizing or copying data.
Enhance partnerships
Enhance partnerships by providing greater data access and participating in data ecosystems.
Monetize your data
Offer value-added services and monetize data in ways not previously possible.
Working with data partners is easier with Apheris
Data Custodian
"When I think about Apheris, I think about acceleration. We were able to accelerate the onboarding of new partners by months, which also leads to much faster development and evolution of our own data products."
Sr. Director Strategic Partnerships Leading Data Aggregator
Resources
Guide
Buyer's Guide to Secure Data Collaboration
Secure data collaborations with multiple parties are the next source of getting a competitive edge. However, only a few data leaders know what to look for to get projects going. This buyer's guide will help you evaluate platforms and highlight considerations for AI and analytics across organizational boundaries.
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.
Infographic
Infographic: What are Collaborative Data Ecosystems?
The world's biggest challenges are going to be solved in Collaborative Data Ecosystems. Learn how in our free-to-access infographic!