Apheris Federated MLOps Platform
Build, deploy and operationalize data products and AI across organizational boundaries, while protecting privacy and IP
The Apheris Platform is the only enterprise-grade solution that covers the full data science workflow & collaboration process across companies
Privacy & IP Protection
Leverage the full potential of partnerships without ever compromising the privacy or IP of any data or model
Compliance
Reduce risk and accelerate time-to-market with streamlined compliance frameworks and processes
Security
Ensure end-to-end protection of all assets with a platform that is designed to be foundationally secure
POSSIBILITIES One simple platform for your most secure data collaborations
Below are the various ways that your data collaborations can take shape
Internal
Collaborate across geographies
BENEFITS 1. Unlock combined insights 2. Empower truly secure data collaboration 3. Scale AI and capture exponential returns
One-to-One
Collaborate with external partners
BENEFITS 1. Combine knowledge to power more valuable AI 2. Establish trust and confidence in partnerships 3. Change the ways you will compete in the future
One-to-Many
Collaborate with multiple partners
BENEFITS 1. Create new revenue-generating business models 2. Mitigate risk and leverage data for valuable AI 3. Outpace competition and accelerate growth
Data Ecosystem
Build data collaborations on global scale
BENEFITS 1. Reach economies of scale by spreading profits & costs 2. Stay up to date with legislation 3. Operationalize AI and become a leader in your industry
How our platform works
Create Nodes
- Secure Nodes are physically isolated and confidential environments where data always stays protected.
- Private data never leaves its Node and all data science computations happen exclusively within it.
Add Data
- Add a dataset to a workspace by selecting a set of files.
- Describe data to understand the scope and origin.
- Add or generate representative test data to enable third parties to explore your data in a privacy-preserving manner.
Create Asset Policies
- Grant data consumers the permission to perform certain operations on a specified list of datasets.
- Set fine-grained privacy controls.
Explore Available Data
- Explore data characteristics through metadata, representative test data and privacy-preserving statistics computations.
- Build your federated computations and test it locally.
Run Federated Computations
- Execute the data science experiment.
- Our propriertary Privacy Guard enforces specific privacy requirements.
- Computations are orchestrated across selected datasets in their respective nodes.
- Node results are aggregated and returned to the user.
Get Insights
- Retrieve the results from the federated computation.
- Analyze the results to generate business insights.
